An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index
|
|
- Hilda Price
- 5 years ago
- Views:
Transcription
1 IJCSES International Journal of Computer Sienes and Engineering Systems, ol., No.4, Otober 2007 CSES International 2007 ISSN An Optimized Approah on Applying Geneti Algorithm to Adaptive Cluster alidity Index Tzu-Chieh LIN, Hsiang-Cheh HUANG 2, Bin-Yih LIAO and Jeng-Shyang PAN Department of Eletroni Engineering, National Kaohsiung University of Applied Sienes Kaohsiung, 807, Taiwan {tlin, byliao, 2 Department of Eletrial Engineering, National University of Kaohsiung Kaohsiung, 8, Taiwan Abstrat The partitioning or lustering method is an important researh branh in data mining area, and it divides the dataset into an arbitrary number of lusters based on the orrelation attribute of all elements of the dataset. Most datasets have the original lusters number, whih is estimated with luster validity index. But most methods give the error estimation for most real datasets. In order to solve this problem, this paper applies the optimization tehnique of geneti algorithm (GA) to the new adaptive luster validity index, whih is alled the Gene Index (GI). The algorithm applies GA to adust the weighting fators of adaptive luster validity index to train an optimal luster validity index. It is tested with many real datasets, and results show the proposed algorithm an give higher performane and aurately estimate the original luster number of real datasets ompared with the urrent luster validity index methods. Key words: Clustering, Geneti Algorithm, Cluster alidity Index, Optimization, Data Mining.. Introdution Data partitioning is ommonly enountered in real appliations. Lots of shemes are proposed to assess the performanes for speifi algorithms in literature. The main onern of data partitioning is how to orretly divide the data points into lusters. Some algorithms in literature are speifially designed for ertain databases. Thus, these may perform well in some ases but not always good in general. In this paper, we would like to propose a generalized sheme, whih is integrated with optimization tehniques, for better partitioning the data. There are a number of indies proposed in literature to assess the performanes of data lustering. The main ideas are twofold: () data points within the same luster should loate as lose as possible, and (2) data points in different lusters should be as apart as possible. Based on the two onepts, a variety of the luster validity indies are proposed. We make neessary simulations and verify that not all the indies perform well. Therefore, we employ the geneti algorithm (GA) [] for resulting in better performanes in data partitioning. This paper is organized as follows. In Setion 2 we point out the data partitioning shemes and the luster validity indies. In Setion 3 we desribe the proposed algorithm by integrating existing indies and training with GA. Simulation results are demonstrated in Setion 4. Finally, we onlude this paper in Setion Data Partitioning Shemes and Cluster alidity Indies In this paper, we employ the fuzzy C-means (FCM) [2] algorithm for data lustering, and then make omparisons among several indies. By using the onepts of fuzzy theory, every data point does not absolutely belong to a ertain luster; it is denoted by a floating number to represent the degree of belonging to a ertain luster. The maor drawbak for FCM or other algorithms is that the orret number of lusters annot be known exatly in advane. Thus, the luster validity indies with several kinds of representations are proposed to evaluate the orret number of lusters. Every index has its advantages and drawbaks. We ite several ommonly enountered indies; then we perform verifiations in Se. 2, and finally ombine the advantages of these indies and propose the geneti-based luster validity index in Se Cluster validity index: PC index PC (partition oeffiient) index [3] was one of the measures used in early days, with the definition in Eq. (): n 2 PC ( U ) = u ik () n k= i= Manusript reeived July 30, Manusript revised September 20, 2007.
2 254 IJCSES International Journal of Computer Sienes and Engineering Systems, ol., No.4, Otober 2007 where u ik denotes the degree of membership of x i in the luster k, x i is the i th of d-dimensional measured data (and we use d=2 here as an example), under the ondition that [, ], i, k u ik 0 ; u =, k. i= ik To assess the effetiveness of lustering algorithm, the larger the PC index value, the better the performane. 2.2 Cluster validity index: PE index PE (partition entropy) index was also proposed in [3], with the definition in Eq. (2): n ( ) = [ ( )] PE U u ik log u (2) ik n k = i= To assess the effetiveness of lustering algorithm, the smaller the PE index value, the better the performane. 2.3 Cluster validity index: XB index The XB index was proposed by Xie and Beni in [4] with the two important onepts of ompatness and separation. For a good lustering result, the data points within the same luster should be as ompat as possible, while any two different lusters should be as far as possible. It an be formulated by Eq. (3): XB n 2 2 u x ν i i = i = ( U, ; X ) 2 n min( νi νk ) = (3) i k where x i is the i th of d-dimensional measured data (and we use d=2 here), v k is the d-dimension enter of the luster. In Eq. (3), the numerator implies the ompatness and the denominator denotes the separation. Therefore, to assess the effetiveness of lustering algorithm, the smaller the XB index value, the better the performane. 2.4 Cluster validity index: K index The K index was proposed by Kwon [5] based on the improvement of the XB index. In Eq. (3), we find when n, XB 0, and it is generally inorret for pratial appliations. By modifying Eq. (3), we obtain Eq. (4): K n u x ν + νi ν i i = i = = ( U, ; X ) 2 min( νi νk ) = (4) i k where ν denotes the geometri enter of data points. To assess the effetiveness of lustering algorithm, the smaller the XB index value, the better the performane. 2.5 Cluster validity index: B rit index B rit index was proposed in [6]. It is also omposed of the ompatness and separation parameters in order to obtain the optimal number of lusters. The measure of ompatness and separation are independently derived. First, the separation between lusters is denoted by G(), max δ( i, ) i, G() = min (5) δ(, ) i i where ( ) δ, is a distane measure between the i geometri enters of lusters i and, with the definition of δ T 2 (, ) ( ) A( ) i = (6) i i and A denotes a positive definite matrix with dimension of d d (or 2 2 here). For simpliity, people use the identity matrix I to replae the matrix A in Eq. (6) to verify the distane measure. Next, the ompatness is represented by the ratio of varianes between the data points of the urrent luster, and the data points within every luster, denoted by wt (), wt () d varq ( k ) q= k = = d vartotal ( q) q= (7) where var q denotes the urrent luster and var total denotes the variane of the whole data set. From experimental results, the value of G() is muh larger than that of wt () with the ranges of G() [0, 20] and wt () [0, 0.8], thus we need to inlude a weighting fator α to balane the effets from both fators, and we obtain B rit( ) = G( ) + α wt ( ) (8) max G( ) where α = denotes the weighting fator. max ( ) wt From derivations above, when the smaller B rit index is obtained, the lustering performane would be better. 2.6 Cluster validity index: S index S index was proposed in [7]. It also adopted the onepts of ompatness and separation. Unlike the B rit index in Se. 0, both fators are normalized to the values between 0 and to balane the effets from both fators. In measuring the ompatness, the mean distane of the lusters in the data set is alulated, u (, ; X ) = i x (9) i= ni x xi where n i denotes the number of data points within luster i, i is the geometri enter of luster i, and the total of mean distanes are alulated. The separation measure is simply denoted by o =, where d d min denotes the min minimum distane between any two lusters.
3 An Optimized Approah on Applying Geneti Algorithm to Adaptive Cluster alidity Index 255 Next, normalization of Eqs. (9) and (0) is performed by u, ; X min[ u, ; X ], ; X =, (0) un on ( ) ( ) ( ) max u (, ; X ) min u o (, ) min[ o (, )] (, ) [ ] [ (, ; X )] =. () max[ (, )] min[ (, )] o o Finally, the S index is defined by, ; X =, ; X,. (2) ( ) ( ) ( ) S un + To assess the effetiveness of lustering algorithm, the smaller the S index value, the better the performane. 2.7 Preliminary results with existing indies To evaluate the effetiveness of existing indies, we generate a two-dimensional, 2000-point, 9-luster testing database alled `My_sample, illustrated in Fig.. All six indies are examined, and results are in Table. With the database, we an expet that the olumn with k=9 should perform the best, i.e., the largest PC value and the smallest values of the other five should be obtained. As we an see, not all of the indies indiate that the orret lustering result is when k=9. Moreover, the riterion for PC is to searh for its maximum value, while for the rest indies the riterion is to find their minimum values. Based on the two findings, the optimization tehniques an be inluded into the lustering algorithm to searh for the better and more orret results. 3. Geneti-based Cluster alidity Index As we an see from Se. 2. to 2.6, every index has its own speifi onept for data lustering and the results in Se. 2.7 have a diversity of performanes. Therefore, we employ geneti algorithm (GA) for finding an optimized result based on the onept of every index above. GA onstitutes of three maor steps: rossover, mutation, and seletion. Based on the fitness funtion, we try to integrate our watermarking sheme with GA proedures. on Table : The index values for lustering from 2 to 0 lusters in six different shemes for My_sample database. The shaded bloks represent the orret lustering results. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S Preproessing in GA We need to have hromosomes to perform the three steps in GA. We employ five popularly used databases, inluding auto-mpg [8], bupa [9], m [0], iris [], and wine [2] in Table 2 for GA optimization. Half of the data set in eah database is used for training, and the other half is used for testing. 3.2 Deiding the fitness funtion After onsidering pratial implementations in GA, and based on the indies desribed in Se. 0 to 0, in this paper, we proposed the geneti-based index for data lustering. The fitness funtion is denoted by INTRA( k ) max d ( i, ) k = i, (, ; X ) = α + β. (3) gene MSDt min d ( i, ) i In the first term, it denotes the ompatness with INTRA k = n t - x, and (4) ( ) t = k x x k MSD t = t - x. (5) nt n In the seond term, d( i, ) is the same as that defined in Eq. (6). Also, α and β are the weighting fators, whih at as the output after GA training. The goal for optimization is to find the minimized value in the fitness funtion. Under the best ondition, the fitness value reahes Proedures in GA training The GA proedures for optimized luster validity index are desribed as follows. Fig. The two-dimensional, 2000-point, 9-luster database My_sample. Step. Produing the hromosomes: 40 hromosomes are produed. Eah hromosome denotes the weighting fators in the fitness funtion, i.e., ( α i, β i), i 40. Beause the fitness funtion is omposed of two opposing onditions, we only onern about the ratio between the two weights; we set 0 α i, β i.
4 256 IJCSES International Journal of Computer Sienes and Engineering Systems, ol., No.4, Otober 2007 Table 2: The five databases used in this paper. Training database # of data points Testing database # of data points auto-mpg_train 96 auto-mpg_test 96 bupa_train 73 bupa_test 72 m_train 737 m_test 736 iris_train 75 iris_test 75 wine_train 89 wine_test 89 Fitness values are alulated from the training databases in Table 2. At the beginning of first iteration, hromosome values are randomly set. In training, hromosome values are modified based on the output of the previous iteration. Step 2. Seleting the better hromosomes: All the 40 sets of hromosomes are inluded into the fitness funtion and the orresponding fitness sores are alulated. The 20 hromosomes with smaller fitness values are kept for use in the next iteration, and the other 20 are disarded. 20 new hromosomes in the next iteration are produed from rossover and mutation based on the 20 hromosomes remained. Step 3. Crossover of hromosome: From the 20 remained hromosomes, we randomly hoose 0 of them, and gather into 5 pairs, to perform the rossover operation. By swapping the α or β values of every pair, 0 new hromosomes are produed. Step 4. Mutation of hromosome: The 0 hromosomes that are not hosen in 0 are used in this step. The α values in the first five hromosomes are replaed by randomly set, new α values. Similar operation is performed on the β values of the other five. Step 5. The stopping ondition: One the pre-determined number of iterations is reahed, or when the fitness value equals 0, the training is stopped, and the weighting fators orresponding to the smallest fitness sore in the final iteration, ( α, β ), is the output. 4. Simulation Results After training for 000 iterations the GA optimization in Se. 3.3, we obtain the optimized weighting fators ( α, β ) = ( 0.856, ). With the two values, we an ompare the GA optimized result with those in Se. 2. to 2.6 by verifying the five test databases in Table 2. We depit the detailed results with the auto-mpg database in Table 3, the bupa database in Table 4, the iris database in Table 3: Index values from 2 to 0 lusters in seven different shemes. Shaded bloks show the orret results for auto-mpg database. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S GI Table 4: Index values from 2 to 0 lusters in seven different shemes. Shaded bloks show the orret results for bupa database. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S GI Table 5, the wine database in Table 6, respetively. Numerial values in Table 3 depit the results for the auto-mpg database, whih has three lusters. We an see that only with the proposed GA-based index has the orret result. In bupa, m, iris, and wine databases, similar results an be obtained, and detailed omparisons an be found from Table 4 to Table 7, respetively. In addition, from Table 8, we see that the proposed GI results in orret luster numbers in four of the five test databases. Comparing to other six indies that only result in one orret luster number, our sheme gets better performane. In addition, regarding to the m database, none of the seven indies have the orret luster number. 5. Conlusion In this paper, we disussed about data lustering shemes and proposed a new luster validity index based on GA. GI index outperforms all the six existing indies in literature. However, lustering results for appliations to some database are not orret even after GA training. And this is the motivation for our researhes in the future. Aknowledgments This work is partially supported by National Siene Counil (Taiwan) under grant NSC E
5 An Optimized Approah on Applying Geneti Algorithm to Adaptive Cluster alidity Index 257 Table 5: Index values from 2 to 0 lusters in seven different shemes. Shaded bloks show the orret results for m database. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S GI Table 6: Index values from 2 to 0 lusters in seven different shemes. Shaded bloks show the orret results for iris database. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S [3] J.C. Bezdek, Pattern Reognition with Fuzzy Obetive Funtion Algorithms, New York, NY: Plenum, 98. [4] X.L. Xie and G. Beni, "A validity measure for fuzzy lustering", IEEE Trans. Patt. Anal. Mahine Intell., ol. 3, No. 8, 99, pp [5] S.H. Kwon, "Cluster validity index for fuzzy lustering", Eletronis Letters, vol. 34, no. 22, pp , 998. [6] A.-O. Boudraa, "Dynami estimation of number of lusters in data sets", Eletronis Letters, ol. 35, No. 9, 999, pp [7] D.-J. Kim, Y.-W. Park, and D.-J. Park, "A novel validity index for determination of the optimal number of lusters", IEICE. Trans. Inf. & Syst., ol. E84-D, No. 2, 200, pp [8] R. Quinlan, "Auto-mpg data", ftp://ftp.is.ui.edu/pub/mahine-learning-databases/auto-mpg/, 993. [9] BUPA Medial Researh Ltd, "BUPA liver disorders", ftp://ftp.is.ui.edu/pub/mahine-learning-databases/liver-disorders/,990. [0] T.S. Lim, "Contraeptive method hoie", ftp://ftp.is.ui.edu/pub/mahine-learning-databases/m/, 999. [] R.A. Fisher, "Iris plants database", ftp://ftp.is.ui.edu/pub/mahine-learning-databases/iris/, 988. [2] S. Aeberhard, "Wine reognition data", ftp://ftp.is.ui.edu/pub/mahine-learning-databases/wine/, 998. GI Table 7: Index values from 2 to 0 lusters in seven different shemes. Shaded bloks show the orret results for wine database. index k=2 k=3 k=4 k=5 k=6 k=7 k=8 k=9 k=0 PC PE XB K B rit S GI Table 8: Comparisons of the seven indies for the five test databases. Our sheme performs the best. Database Original lusters PC PE XB K B rit S GA auto-mpg bupa m iris wine Referenes [] D.E. Goldberg, Geneti Algorithms in Searh, Optimization and Mahine Learning, Boston, MA: Kluwer, 989. [2] J.C. Bezdek, R. Ehrlih, and W. Full, "FCM: Fuzzy C- Means Algorithm", Computers and Geosienes, ol. 0, No. 2-3, 984, pp
6
A Novel Validity Index for Determination of the Optimal Number of Clusters
IEICE TRANS. INF. & SYST., VOL.E84 D, NO.2 FEBRUARY 2001 281 LETTER A Novel Validity Index for Determination of the Optimal Number of Clusters Do-Jong KIM, Yong-Woon PARK, and Dong-Jo PARK, Nonmembers
More informationA {k, n}-secret Sharing Scheme for Color Images
A {k, n}-seret Sharing Sheme for Color Images Rastislav Luka, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos The Edward S. Rogers Sr. Dept. of Eletrial and Computer Engineering, University
More informationThe Minimum Redundancy Maximum Relevance Approach to Building Sparse Support Vector Machines
The Minimum Redundany Maximum Relevane Approah to Building Sparse Support Vetor Mahines Xiaoxing Yang, Ke Tang, and Xin Yao, Nature Inspired Computation and Appliations Laboratory (NICAL), Shool of Computer
More informationAbstract. Key Words: Image Filters, Fuzzy Filters, Order Statistics Filters, Rank Ordered Mean Filters, Channel Noise. 1.
Fuzzy Weighted Rank Ordered Mean (FWROM) Filters for Mixed Noise Suppression from Images S. Meher, G. Panda, B. Majhi 3, M.R. Meher 4,,4 Department of Eletronis and I.E., National Institute of Tehnology,
More informationA DYNAMIC ACCESS CONTROL WITH BINARY KEY-PAIR
Malaysian Journal of Computer Siene, Vol 10 No 1, June 1997, pp 36-41 A DYNAMIC ACCESS CONTROL WITH BINARY KEY-PAIR Md Rafiqul Islam, Harihodin Selamat and Mohd Noor Md Sap Faulty of Computer Siene and
More informationNew Fuzzy Object Segmentation Algorithm for Video Sequences *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 521-537 (2008) New Fuzzy Obet Segmentation Algorithm for Video Sequenes * KUO-LIANG CHUNG, SHIH-WEI YU, HSUEH-JU YEH, YONG-HUAI HUANG AND TA-JEN YAO Department
More informationthe data. Structured Principal Component Analysis (SPCA)
Strutured Prinipal Component Analysis Kristin M. Branson and Sameer Agarwal Department of Computer Siene and Engineering University of California, San Diego La Jolla, CA 9193-114 Abstrat Many tasks involving
More informationCalculation of typical running time of a branch-and-bound algorithm for the vertex-cover problem
Calulation of typial running time of a branh-and-bound algorithm for the vertex-over problem Joni Pajarinen, Joni.Pajarinen@iki.fi Otober 21, 2007 1 Introdution The vertex-over problem is one of a olletion
More informationGray Codes for Reflectable Languages
Gray Codes for Refletable Languages Yue Li Joe Sawada Marh 8, 2008 Abstrat We lassify a type of language alled a refletable language. We then develop a generi algorithm that an be used to list all strings
More informationCluster-Based Cumulative Ensembles
Cluster-Based Cumulative Ensembles Hanan G. Ayad and Mohamed S. Kamel Pattern Analysis and Mahine Intelligene Lab, Eletrial and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1,
More informationPerformance of Histogram-Based Skin Colour Segmentation for Arms Detection in Human Motion Analysis Application
World Aademy of Siene, Engineering and Tehnology 8 009 Performane of Histogram-Based Skin Colour Segmentation for Arms Detetion in Human Motion Analysis Appliation Rosalyn R. Porle, Ali Chekima, Farrah
More informationPipelined Multipliers for Reconfigurable Hardware
Pipelined Multipliers for Reonfigurable Hardware Mithell J. Myjak and José G. Delgado-Frias Shool of Eletrial Engineering and Computer Siene, Washington State University Pullman, WA 99164-2752 USA {mmyjak,
More informationAn Alternative Approach to the Fuzzifier in Fuzzy Clustering to Obtain Better Clustering Results
An Alternative Approah to the Fuzziier in Fuzzy Clustering to Obtain Better Clustering Results Frank Klawonn Department o Computer Siene University o Applied Sienes BS/WF Salzdahlumer Str. 46/48 D-38302
More informationImproved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules
Improved Vehile Classifiation in Long Traffi Video by Cooperating Traker and Classifier Modules Brendan Morris and Mohan Trivedi University of California, San Diego San Diego, CA 92093 {b1morris, trivedi}@usd.edu
More informationA Coarse-to-Fine Classification Scheme for Facial Expression Recognition
A Coarse-to-Fine Classifiation Sheme for Faial Expression Reognition Xiaoyi Feng 1,, Abdenour Hadid 1 and Matti Pietikäinen 1 1 Mahine Vision Group Infoteh Oulu and Dept. of Eletrial and Information Engineering
More informationCapturing Large Intra-class Variations of Biometric Data by Template Co-updating
Capturing Large Intra-lass Variations of Biometri Data by Template Co-updating Ajita Rattani University of Cagliari Piazza d'armi, Cagliari, Italy ajita.rattani@diee.unia.it Gian Lua Marialis University
More informationModel Based Approach for Content Based Image Retrievals Based on Fusion and Relevancy Methodology
The International Arab Journal of Information Tehnology, Vol. 12, No. 6, November 15 519 Model Based Approah for Content Based Image Retrievals Based on Fusion and Relevany Methodology Telu Venkata Madhusudhanarao
More informationOn - Line Path Delay Fault Testing of Omega MINs M. Bellos 1, E. Kalligeros 1, D. Nikolos 1,2 & H. T. Vergos 1,2
On - Line Path Delay Fault Testing of Omega MINs M. Bellos, E. Kalligeros, D. Nikolos,2 & H. T. Vergos,2 Dept. of Computer Engineering and Informatis 2 Computer Tehnology Institute University of Patras,
More informationDetection and Recognition of Non-Occluded Objects using Signature Map
6th WSEAS International Conferene on CIRCUITS, SYSTEMS, ELECTRONICS,CONTROL & SIGNAL PROCESSING, Cairo, Egypt, De 9-31, 007 65 Detetion and Reognition of Non-Oluded Objets using Signature Map Sangbum Park,
More informationIMPROVED FUZZY CLUSTERING METHOD BASED ON INTUITIONISTIC FUZZY PARTICLE SWARM OPTIMIZATION
Journal of Theoretial and Applied Information Tehnology IMPROVED FUZZY CLUSTERING METHOD BASED ON INTUITIONISTIC FUZZY PARTICLE SWARM OPTIMIZATION V.KUMUTHA, 2 S. PALANIAMMAL D.J. Aademy For Managerial
More informationA Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering
A Novel Bit Level Time Series Representation with Impliation of Similarity Searh and lustering hotirat Ratanamahatana, Eamonn Keogh, Anthony J. Bagnall 2, and Stefano Lonardi Dept. of omputer Siene & Engineering,
More informationKERNEL SPARSE REPRESENTATION WITH LOCAL PATTERNS FOR FACE RECOGNITION
KERNEL SPARSE REPRESENTATION WITH LOCAL PATTERNS FOR FACE RECOGNITION Cuiui Kang 1, Shengai Liao, Shiming Xiang 1, Chunhong Pan 1 1 National Laboratory of Pattern Reognition, Institute of Automation, Chinese
More informationApproximate logic synthesis for error tolerant applications
Approximate logi synthesis for error tolerant appliations Doohul Shin and Sandeep K. Gupta Eletrial Engineering Department, University of Southern California, Los Angeles, CA 989 {doohuls, sandeep}@us.edu
More informationWeak Dependence on Initialization in Mixture of Linear Regressions
Proeedings of the International MultiConferene of Engineers and Computer Sientists 8 Vol I IMECS 8, Marh -6, 8, Hong Kong Weak Dependene on Initialization in Mixture of Linear Regressions Ryohei Nakano
More informationUnsupervised Stereoscopic Video Object Segmentation Based on Active Contours and Retrainable Neural Networks
Unsupervised Stereosopi Video Objet Segmentation Based on Ative Contours and Retrainable Neural Networks KLIMIS NTALIANIS, ANASTASIOS DOULAMIS, and NIKOLAOS DOULAMIS National Tehnial University of Athens
More informationBoosted Random Forest
Boosted Random Forest Yohei Mishina, Masamitsu suhiya and Hironobu Fujiyoshi Department of Computer Siene, Chubu University, 1200 Matsumoto-ho, Kasugai, Aihi, Japan {mishi, mtdoll}@vision.s.hubu.a.jp,
More informationDr.Hazeem Al-Khafaji Dept. of Computer Science, Thi-Qar University, College of Science, Iraq
Volume 4 Issue 6 June 014 ISSN: 77 18X International Journal of Advaned Researh in Computer Siene and Software Engineering Researh Paper Available online at: www.ijarsse.om Medial Image Compression using
More informationAcoustic Links. Maximizing Channel Utilization for Underwater
Maximizing Channel Utilization for Underwater Aousti Links Albert F Hairris III Davide G. B. Meneghetti Adihele Zorzi Department of Information Engineering University of Padova, Italy Email: {harris,davide.meneghetti,zorzi}@dei.unipd.it
More informationExtracting Partition Statistics from Semistructured Data
Extrating Partition Statistis from Semistrutured Data John N. Wilson Rihard Gourlay Robert Japp Mathias Neumüller Department of Computer and Information Sienes University of Strathlyde, Glasgow, UK {jnw,rsg,rpj,mathias}@is.strath.a.uk
More informationPlot-to-track correlation in A-SMGCS using the target images from a Surface Movement Radar
Plot-to-trak orrelation in A-SMGCS using the target images from a Surfae Movement Radar G. Golino Radar & ehnology Division AMS, Italy ggolino@amsjv.it Abstrat he main topi of this paper is the formulation
More informationA Hybrid Neuro-Genetic Approach to Short-Term Traffic Volume Prediction
A Hybrid Neuro-Geneti Approah to Short-Term Traffi Volume Predition 1. Introdution Shahriar Afandizadeh 1,*, Jalil Kianfar 2 Reeived: January 2003, Revised: July 2008, Aepted: January 2009 Abstrat: This
More informationA Load-Balanced Clustering Protocol for Hierarchical Wireless Sensor Networks
International Journal of Advanes in Computer Networks and Its Seurity IJCNS A Load-Balaned Clustering Protool for Hierarhial Wireless Sensor Networks Mehdi Tarhani, Yousef S. Kavian, Saman Siavoshi, Ali
More informationVolume 3, Issue 9, September 2013 International Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 9, September 2013 ISSN: 2277 128X International Journal of Advaned Researh in Computer Siene and Software Engineering Researh Paper Available online at: www.ijarsse.om A New-Fangled Algorithm
More informationCross-layer Resource Allocation on Broadband Power Line Based on Novel QoS-priority Scheduling Function in MAC Layer
Communiations and Networ, 2013, 5, 69-73 http://dx.doi.org/10.4236/n.2013.53b2014 Published Online September 2013 (http://www.sirp.org/journal/n) Cross-layer Resoure Alloation on Broadband Power Line Based
More informationPreliminary investigation of multi-wavelet denoising in partial discharge detection
Preliminary investigation of multi-wavelet denoising in partial disharge detetion QIAN YONG Department of Eletrial Engineering Shanghai Jiaotong University 8#, Donghuan Road, Minhang distrit, Shanghai
More informationarxiv: v1 [cs.db] 13 Sep 2017
An effiient lustering algorithm from the measure of loal Gaussian distribution Yuan-Yen Tai (Dated: May 27, 2018) In this paper, I will introdue a fast and novel lustering algorithm based on Gaussian distribution
More informationA Multi-Head Clustering Algorithm in Vehicular Ad Hoc Networks
International Journal of Computer Theory and Engineering, Vol. 5, No. 2, April 213 A Multi-Head Clustering Algorithm in Vehiular Ad Ho Networks Shou-Chih Lo, Yi-Jen Lin, and Jhih-Siao Gao Abstrat Clustering
More informationAnalysis of input and output configurations for use in four-valued CCD programmable logic arrays
nalysis of input and output onfigurations for use in four-valued D programmable logi arrays J.T. utler H.G. Kerkhoff ndexing terms: Logi, iruit theory and design, harge-oupled devies bstrat: s in binary,
More informationSelf-Adaptive Parent to Mean-Centric Recombination for Real-Parameter Optimization
Self-Adaptive Parent to Mean-Centri Reombination for Real-Parameter Optimization Kalyanmoy Deb and Himanshu Jain Department of Mehanial Engineering Indian Institute of Tehnology Kanpur Kanpur, PIN 86 {deb,hjain}@iitk.a.in
More informationMulti Features Content-Based Image Retrieval Using Clustering and Decision Tree Algorithm
TELKOMNIKA, Vol.4, No.4, Deember 06, pp. 480~49 ISSN: 693-6930, aredited A by DIKTI, Deree No: 58/DIKTI/Kep/03 DOI: 0.98/TELKOMNIKA.v4i4.4646 480 Multi Features Content-Based Image Retrieval Using Clustering
More information1. Introduction. 2. The Probable Stope Algorithm
1. Introdution Optimization in underground mine design has reeived less attention than that in open pit mines. This is mostly due to the diversity o underground mining methods and omplexity o underground
More informationDynamic Algorithms Multiple Choice Test
3226 Dynami Algorithms Multiple Choie Test Sample test: only 8 questions 32 minutes (Real test has 30 questions 120 minutes) Årskort Name Eah of the following 8 questions has 4 possible answers of whih
More informationA scheme for racquet sports video analysis with the combination of audio-visual information
A sheme for raquet sports video analysis with the ombination of audio-visual information Liyuan Xing a*, Qixiang Ye b, Weigang Zhang, Qingming Huang a and Hua Yu a a Graduate Shool of the Chinese Aadamy
More informationOptimization of Two-Stage Cylindrical Gear Reducer with Adaptive Boundary Constraints
5 JOURNAL OF SOFTWARE VOL. 8 NO. 8 AUGUST Optimization of Two-Stage Cylindrial Gear Reduer with Adaptive Boundary Constraints Xueyi Li College of Mehanial and Eletroni Engineering Shandong University of
More informationPerformance Improvement of TCP on Wireless Cellular Networks by Adaptive FEC Combined with Explicit Loss Notification
erformane Improvement of TC on Wireless Cellular Networks by Adaptive Combined with Expliit Loss tifiation Masahiro Miyoshi, Masashi Sugano, Masayuki Murata Department of Infomatis and Mathematial Siene,
More informationarxiv: v1 [cs.gr] 10 Apr 2015
REAL-TIME TOOL FOR AFFINE TRANSFORMATIONS OF TWO DIMENSIONAL IFS FRACTALS ELENA HADZIEVA AND MARIJA SHUMINOSKA arxiv:1504.02744v1 s.gr 10 Apr 2015 Abstrat. This work introdues a novel tool for interative,
More informationCluster Centric Fuzzy Modeling
10.1109/TFUZZ.014.300134, IEEE Transations on Fuzzy Systems TFS-013-0379.R1 1 Cluster Centri Fuzzy Modeling Witold Pedryz, Fellow, IEEE, and Hesam Izakian, Student Member, IEEE Abstrat In this study, we
More informationUsing Augmented Measurements to Improve the Convergence of ICP
Using Augmented Measurements to Improve the onvergene of IP Jaopo Serafin, Giorgio Grisetti Dept. of omputer, ontrol and Management Engineering, Sapienza University of Rome, Via Ariosto 25, I-0085, Rome,
More informationFUZZY WATERSHED FOR IMAGE SEGMENTATION
FUZZY WATERSHED FOR IMAGE SEGMENTATION Ramón Moreno, Manuel Graña Computational Intelligene Group, Universidad del País Vaso, Spain http://www.ehu.es/winto; {ramon.moreno,manuel.grana}@ehu.es Abstrat The
More informationStable Road Lane Model Based on Clothoids
Stable Road Lane Model Based on Clothoids C Gakstatter*, S Thomas**, Dr P Heinemann*, Prof Gudrun Klinker*** *Audi Eletronis Venture GmbH, **Leibniz Universität Hannover, ***Tehnishe Universität Münhen
More informationA Dual-Hamiltonian-Path-Based Multicasting Strategy for Wormhole-Routed Star Graph Interconnection Networks
A Dual-Hamiltonian-Path-Based Multiasting Strategy for Wormhole-Routed Star Graph Interonnetion Networks Nen-Chung Wang Department of Information and Communiation Engineering Chaoyang University of Tehnology,
More informationExploiting Enriched Contextual Information for Mobile App Classification
Exploiting Enrihed Contextual Information for Mobile App Classifiation Hengshu Zhu 1 Huanhuan Cao 2 Enhong Chen 1 Hui Xiong 3 Jilei Tian 2 1 University of Siene and Tehnology of China 2 Nokia Researh Center
More informationParticle Swarm Optimization for the Design of High Diffraction Efficient Holographic Grating
Original Artile Partile Swarm Optimization for the Design of High Diffration Effiient Holographi Grating A.K. Tripathy 1, S.K. Das, M. Sundaray 3 and S.K. Tripathy* 4 1, Department of Computer Siene, Berhampur
More informationUsing Game Theory and Bayesian Networks to Optimize Cooperation in Ad Hoc Wireless Networks
Using Game Theory and Bayesian Networks to Optimize Cooperation in Ad Ho Wireless Networks Giorgio Quer, Federio Librino, Lua Canzian, Leonardo Badia, Mihele Zorzi, University of California San Diego La
More informationAlgorithms, Mechanisms and Procedures for the Computer-aided Project Generation System
Algorithms, Mehanisms and Proedures for the Computer-aided Projet Generation System Anton O. Butko 1*, Aleksandr P. Briukhovetskii 2, Dmitry E. Grigoriev 2# and Konstantin S. Kalashnikov 3 1 Department
More informationA Partial Sorting Algorithm in Multi-Hop Wireless Sensor Networks
A Partial Sorting Algorithm in Multi-Hop Wireless Sensor Networks Abouberine Ould Cheikhna Department of Computer Siene University of Piardie Jules Verne 80039 Amiens Frane Ould.heikhna.abouberine @u-piardie.fr
More informationTUMOR DETECTION IN MRI BRAIN IMAGE SEGMENTATION USING PHASE CONGRUENCY MODIFIED FUZZY C MEAN ALGORITHM
TUMOR DETECTION IN MRI BRAIN IMAGE SEGMENTATION USING PHASE CONGRUENCY MODIFIED FUZZY C MEAN ALGORITHM M. Murugeswari 1, M.Gayathri 2 1 Assoiate Professor, 2 PG Sholar 1,2 K.L.N College of Information
More informationDetecting Outliers in High-Dimensional Datasets with Mixed Attributes
Deteting Outliers in High-Dimensional Datasets with Mixed Attributes A. Koufakou, M. Georgiopoulos, and G.C. Anagnostopoulos 2 Shool of EECS, University of Central Florida, Orlando, FL, USA 2 Dept. of
More informationDynamic Backlight Adaptation for Low Power Handheld Devices 1
Dynami Baklight Adaptation for ow Power Handheld Devies 1 Sudeep Pasriha, Manev uthra, Shivajit Mohapatra, Nikil Dutt and Nalini Venkatasubramanian 444, Computer Siene Building, Shool of Information &
More informationNONLINEAR BACK PROJECTION FOR TOMOGRAPHIC IMAGE RECONSTRUCTION. Ken Sauer and Charles A. Bouman
NONLINEAR BACK PROJECTION FOR TOMOGRAPHIC IMAGE RECONSTRUCTION Ken Sauer and Charles A. Bouman Department of Eletrial Engineering, University of Notre Dame Notre Dame, IN 46556, (219) 631-6999 Shool of
More informationChapter 2: Introduction to Maple V
Chapter 2: Introdution to Maple V 2-1 Working with Maple Worksheets Try It! (p. 15) Start a Maple session with an empty worksheet. The name of the worksheet should be Untitled (1). Use one of the standard
More informationGradient based progressive probabilistic Hough transform
Gradient based progressive probabilisti Hough transform C.Galambos, J.Kittler and J.Matas Abstrat: The authors look at the benefits of exploiting gradient information to enhane the progressive probabilisti
More informationMultiple-Criteria Decision Analysis: A Novel Rank Aggregation Method
3537 Multiple-Criteria Deision Analysis: A Novel Rank Aggregation Method Derya Yiltas-Kaplan Department of Computer Engineering, Istanbul University, 34320, Avilar, Istanbul, Turkey Email: dyiltas@ istanbul.edu.tr
More informationAlgorithms for External Memory Lecture 6 Graph Algorithms - Weighted List Ranking
Algorithms for External Memory Leture 6 Graph Algorithms - Weighted List Ranking Leturer: Nodari Sithinava Sribe: Andi Hellmund, Simon Ohsenreither 1 Introdution & Motivation After talking about I/O-effiient
More informationCOST PERFORMANCE ASPECTS OF CCD FAST AUXILIARY MEMORY
COST PERFORMANCE ASPECTS OF CCD FAST AUXILIARY MEMORY Dileep P, Bhondarkor Texas Instruments Inorporated Dallas, Texas ABSTRACT Charge oupled devies (CCD's) hove been mentioned as potential fast auxiliary
More informationGraph-Based vs Depth-Based Data Representation for Multiview Images
Graph-Based vs Depth-Based Data Representation for Multiview Images Thomas Maugey, Antonio Ortega, Pasal Frossard Signal Proessing Laboratory (LTS), Eole Polytehnique Fédérale de Lausanne (EPFL) Email:
More informationSpatial-Aware Collaborative Representation for Hyperspectral Remote Sensing Image Classification
Spatial-Aware Collaborative Representation for Hyperspetral Remote Sensing Image ifiation Junjun Jiang, Member, IEEE, Chen Chen, Member, IEEE, Yi Yu, Xinwei Jiang, and Jiayi Ma Member, IEEE Representation-residual
More informationMulti-Channel Wireless Networks: Capacity and Protocols
Multi-Channel Wireless Networks: Capaity and Protools Tehnial Report April 2005 Pradeep Kyasanur Dept. of Computer Siene, and Coordinated Siene Laboratory, University of Illinois at Urbana-Champaign Email:
More informationMean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator
1 Mean Deviation Similarity Index: Effiient and Reliable Full-Referene Image Quality Evaluator Hossein Ziaei Nafhi, Atena Shahkolaei, Rahid Hedjam, and Mohamed Cheriet, Senior Member, IEEE two images of
More informationAdapting K-Medians to Generate Normalized Cluster Centers
Adapting -Medians to Generate Normalized Cluster Centers Benamin J. Anderson, Deborah S. Gross, David R. Musiant Anna M. Ritz, Thomas G. Smith, Leah E. Steinberg Carleton College andersbe@gmail.om, {dgross,
More informationNaïve Bayesian Rough Sets Under Fuzziness
IJMSA: Vol. 6, No. 1-2, January-June 2012, pp. 19 25 Serials Publiations ISSN: 0973-6786 Naïve ayesian Rough Sets Under Fuzziness G. GANSAN 1,. KRISHNAVNI 2 T. HYMAVATHI 3 1,2,3 Department of Mathematis,
More informationSINCE the successful launch of high-resolution sensors and
1458 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 Classifiation of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features Yindi
More informationAn Approach to Physics Based Surrogate Model Development for Application with IDPSA
An Approah to Physis Based Surrogate Model Development for Appliation with IDPSA Ignas Mikus a*, Kaspar Kööp a, Marti Jeltsov a, Yuri Vorobyev b, Walter Villanueva a, and Pavel Kudinov a a Royal Institute
More informationTime delay estimation of reverberant meeting speech: on the use of multichannel linear prediction
University of Wollongong Researh Online Faulty of Informatis - apers (Arhive) Faulty of Engineering and Information Sienes 7 Time delay estimation of reverberant meeting speeh: on the use of multihannel
More informationRAC 2 E: Novel Rendezvous Protocol for Asynchronous Cognitive Radios in Cooperative Environments
21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communiations 1 RAC 2 E: Novel Rendezvous Protool for Asynhronous Cognitive Radios in Cooperative Environments Valentina Pavlovska,
More informationAn Efficient and Scalable Approach to CNN Queries in a Road Network
An Effiient and Salable Approah to CNN Queries in a Road Network Hyung-Ju Cho Chin-Wan Chung Dept. of Eletrial Engineering & Computer Siene Korea Advaned Institute of Siene and Tehnology 373- Kusong-dong,
More informationEvolutionary Feature Synthesis for Image Databases
Evolutionary Feature Synthesis for Image Databases Anlei Dong, Bir Bhanu, Yingqiang Lin Center for Researh in Intelligent Systems University of California, Riverside, California 92521, USA {adong, bhanu,
More informationCleanUp: Improving Quadrilateral Finite Element Meshes
CleanUp: Improving Quadrilateral Finite Element Meshes Paul Kinney MD-10 ECC P.O. Box 203 Ford Motor Company Dearborn, MI. 8121 (313) 28-1228 pkinney@ford.om Abstrat: Unless an all quadrilateral (quad)
More informationSegmentation of brain MR image using fuzzy local Gaussian mixture model with bias field correction
IOSR Journal of VLSI and Signal Proessing (IOSR-JVSP) Volume 2, Issue 2 (Mar. Apr. 2013), PP 35-41 e-issn: 2319 4200, p-issn No. : 2319 4197 Segmentation of brain MR image using fuzzy loal Gaussian mixture
More informationSURVEY ON MEDICAL IMAGE SEGMENTATION USING ENHANCED K-MEANS AND KERNELIZED FUZZY C- MEANS
SURVEY ON MEDICAL IMAGE SEGMENTATION USING ENHANCED K-MEANS AND KERNELIZED FUZZY C- MEANS Gunwanti S. Mahajan & Kanhan S. Bhagat. Dept of E &TC, J. T. Mahajan C.o.E Faizpur, India ABSTRACT Diagnosti imaging
More informationSINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs)
48 J. ICT Res. Appl., Vol. 9, No., 5, 48-6 SINR-based Network Seletion for Optimization in Heterogeneous Wireless Networks (HWNs) Abubakar M. Miyim, Mahamod Ismail & Rosdiadee Nordin Department of Eletrial,
More informationSpace- and Time-Efficient BDD Construction via Working Set Control
Spae- and Time-Effiient BDD Constrution via Working Set Control Bwolen Yang Yirng-An Chen Randal E. Bryant David R. O Hallaron Computer Siene Department Carnegie Mellon University Pittsburgh, PA 15213.
More informationThe Implementation of RRTs for a Remote-Controlled Mobile Robot
ICCAS5 June -5, KINEX, Gyeonggi-Do, Korea he Implementation of RRs for a Remote-Controlled Mobile Robot Chi-Won Roh*, Woo-Sub Lee **, Sung-Chul Kang *** and Kwang-Won Lee **** * Intelligent Robotis Researh
More informationA Fast Kernel-based Multilevel Algorithm for Graph Clustering
A Fast Kernel-based Multilevel Algorithm for Graph Clustering Inderjit Dhillon Dept. of Computer Sienes University of Texas at Austin Austin, TX 78712 inderjit@s.utexas.edu Yuqiang Guan Dept. of Computer
More informationTriangles. Learning Objectives. Pre-Activity
Setion 3.2 Pre-tivity Preparation Triangles Geena needs to make sure that the dek she is building is perfetly square to the brae holding the dek in plae. How an she use geometry to ensure that the boards
More informationThe Mathematics of Simple Ultrasonic 2-Dimensional Sensing
The Mathematis of Simple Ultrasoni -Dimensional Sensing President, Bitstream Tehnology The Mathematis of Simple Ultrasoni -Dimensional Sensing Introdution Our ompany, Bitstream Tehnology, has been developing
More informationImproved flooding of broadcast messages using extended multipoint relaying
Improved flooding of broadast messages using extended multipoint relaying Pere Montolio Aranda a, Joaquin Garia-Alfaro a,b, David Megías a a Universitat Oberta de Catalunya, Estudis d Informàtia, Mulimèdia
More informationTest Case Generation from UML State Machines
Test Case Generation from UML State Mahines Dirk Seifert To ite this version: Dirk Seifert. Test Case Generation from UML State Mahines. [Researh Report] 2008. HAL Id: inria-00268864
More informationA New RBFNDDA-KNN Network and Its Application to Medical Pattern Classification
A New RBFNDDA-KNN Network and Its Appliation to Medial Pattern Classifiation Shing Chiang Tan 1*, Chee Peng Lim 2, Robert F. Harrison 3, R. Lee Kennedy 4 1 Faulty of Information Siene and Tehnology, Multimedia
More informationPartial Character Decoding for Improved Regular Expression Matching in FPGAs
Partial Charater Deoding for Improved Regular Expression Mathing in FPGAs Peter Sutton Shool of Information Tehnology and Eletrial Engineering The University of Queensland Brisbane, Queensland, 4072, Australia
More informationSmooth Trajectory Planning Along Bezier Curve for Mobile Robots with Velocity Constraints
Smooth Trajetory Planning Along Bezier Curve for Mobile Robots with Veloity Constraints Gil Jin Yang and Byoung Wook Choi Department of Eletrial and Information Engineering Seoul National University of
More informationHEXA: Compact Data Structures for Faster Packet Processing
Washington University in St. Louis Washington University Open Sholarship All Computer Siene and Engineering Researh Computer Siene and Engineering Report Number: 27-26 27 HEXA: Compat Data Strutures for
More informationSystem-Level Parallelism and Throughput Optimization in Designing Reconfigurable Computing Applications
System-Level Parallelism and hroughput Optimization in Designing Reonfigurable Computing Appliations Esam El-Araby 1, Mohamed aher 1, Kris Gaj 2, arek El-Ghazawi 1, David Caliga 3, and Nikitas Alexandridis
More information13.1 Numerical Evaluation of Integrals Over One Dimension
13.1 Numerial Evaluation of Integrals Over One Dimension A. Purpose This olletion of subprograms estimates the value of the integral b a f(x) dx where the integrand f(x) and the limits a and b are supplied
More informationFOREGROUND OBJECT EXTRACTION USING FUZZY C MEANS WITH BIT-PLANE SLICING AND OPTICAL FLOW
FOREGROUND OBJECT EXTRACTION USING FUZZY C EANS WITH BIT-PLANE SLICING AND OPTICAL FLOW SIVAGAI., REVATHI.T, JEGANATHAN.L 3 APSG, SCSE, VIT University, Chennai, India JRF, DST, Dehi, India. 3 Professor,
More informationLearning Convention Propagation in BeerAdvocate Reviews from a etwork Perspective. Abstract
CS 9 Projet Final Report: Learning Convention Propagation in BeerAdvoate Reviews from a etwork Perspetive Abstrat We look at the way onventions propagate between reviews on the BeerAdvoate dataset, and
More informationtimestamp, if silhouette(x, y) 0 0 if silhouette(x, y) = 0, mhi(x, y) = and mhi(x, y) < timestamp - duration mhi(x, y), else
3rd International Conferene on Multimedia Tehnolog(ICMT 013) An Effiient Moving Target Traking Strateg Based on OpenCV and CAMShift Theor Dongu Li 1 Abstrat Image movement involved bakground movement and
More informationTrajectory Tracking Control for A Wheeled Mobile Robot Using Fuzzy Logic Controller
Trajetory Traking Control for A Wheeled Mobile Robot Using Fuzzy Logi Controller K N FARESS 1 M T EL HAGRY 1 A A EL KOSY 2 1 Eletronis researh institute, Cairo, Egypt 2 Faulty of Engineering, Cairo University,
More informationrepresent = as a finite deimal" either in base 0 or in base. We an imagine that the omputer first omputes the mathematial = then rounds the result to
Sientifi Computing Chapter I Computer Arithmeti Jonathan Goodman Courant Institute of Mathemaial Sienes Last revised January, 00 Introdution One of the many soures of error in sientifi omputing is inexat
More informationVideo Data and Sonar Data: Real World Data Fusion Example
14th International Conferene on Information Fusion Chiago, Illinois, USA, July 5-8, 2011 Video Data and Sonar Data: Real World Data Fusion Example David W. Krout Applied Physis Lab dkrout@apl.washington.edu
More information