A Similarity Measure Method for Symbolization Time Series
|
|
- Samson Merritt
- 6 years ago
- Views:
Transcription
1 Research Journal of Appled Scences, Engneerng and Technology 5(5): , 2013 ISSN: ; e-issn: Maxwell Scentfc Organzaton, 2013 Submtted: July 27, 2012 Accepted: September 03, 2012 Publshed: February 11, 2013 A Smlarty Measure Method for Symbolzaton Tme Seres Qang Nu and Zhgang L Department of Computer Scence and Technology, Chna Unversty of Mnng and Technology, Xuzhou , Chna Abstract: Smlarty measure s the base tas of tme seres data mnng tass. LCSS measure method has obvous lmtatons n the two dfferent length tme seres selecton of a lnear functon. The ELCS measure method s proposed to alze the sequence, whch ntroducng the scale factor to lmt the search path of the smlarty matrx. Experment n herarchcal clusterng algorthm shows that the mproved measure maes up for the shortcomngs of LCSS, mproves the effcency and accuracy of clusterng and mproves tme complexty. Keywords: Herarchcal cluster, LCSS, smlarty measure, tme seres INTRODUCTION Tme seres has always been an mportant and nterestng research feld due to ts frequent appearance n dfferent applcatons. Tme seres smlarty measure that proposed by Agrawal et al. (1993) has become a hot research topc due to ts wde applcaton usages such as tme seres classfcaton, clusterng, abal fndngs on the bass of data mnng, Many methods have been developed for searchng tme seres measure method n large data sets and especally smlarty measure of tme seres s a very mportant tas n the process of data mnng. There s smlarty measure methods of tme seres, such as Faloutsos et al. (1994) proposed a fast subsequence matchng method based on the Eucldean dstance metrc, n whch the smlarty measure of the two tme seres s calculated as two ponts of the same dmenson and t sets a threshold to udge whether the result s smlar. Eucldean dstance requres two sequences of equal length and gnored the temporal characterstcs of tme seres, thus lmtng ts applcaton n tme seres smlarty measure. Chung et al. (2004) uses the weght method n the Eucldean dstance method and elmnates transform offset, but there are parameters set by manual nterventon. Berndt and Clford, (1994) ntroduce Dynamc Tme Warpng dstance (DTW) to the tme seres smlarty measure whch performed well n the local characterstcs comparaton of the two unequal length sequences, but the tme consumpton of the algorthm s too expensve. In addton, DTW algorthm can't found two tme seres peas between low pont and nflecton pont, such as the correspondng relatons between the feature ponts and the accuracy of the algorthm s low. Some researchers (Y et al., 1998; Km et al., 2001) mproved DTW by ntroducng the ndex technology, mang ts tme complexty reduced. An ndex-based approach for smlarty search supportng tme warpng n large sequence databases (Km et al., 2001) proposed the Segment-wse the Tme Warpng dstance (STW), mang the DTW tme complexty decreased greatly, but mang the smlarty measure accuracy reduced too. Latec et al. (2005) put forward a nd of mnmum varance matchng method to obtan the flexble smlarty matchng. In 1994, the Longest Common Subsequence (LCS) (Paterson and Danc, 1994) to the tme seres smlarty measure. Bollobas et al. (1997) put forward LCSS on the bass of LCS, mang a better smlarty measure of tme seres whch have ampltude translaton, tmelne stretchng and bendng deformaton. Some other researchers have proposed the slopebased, the model-based and the event-based smlarty measure. Ths research studes the smlarty measure problem of symbolc tme seres. Frstly, ths study ntroduces the defnton and the classcal smlarty measure. Then, we propose a new smlarty measure algorthm based on the LCSS algorthm: dfferent to the LCSS algorthm, the new algorthm avods the selecton of a lnear functon effectvely, mproves the accuracy of measurement and mproves tme effcency greatly compared to the DTW measure. Fnally, experments to verfy the proposed algorthm. LCS AND LCSS SIMILARITY MEASURE LCS measure: There are tme seres samples X, Y A, ther vector form s: X { x, x,..., x n, Y { y, y,..., y n, they satsfy the longest common subsequence of the followng condtons were X' { x, x,..., x and 1 2 l Correspondng Author: Qang Nu, Department of Computer Scence and Technology, Chna Unversty of Mnng and Technology, Xuzhou , Chna 1726
2 Res. J. Appl. Sc. Eng. Technol., 5(5): , 2013 Y' { y, y,..., y 1 2 l, where l s the length of the Common subsequence, Smlarty between tme seres X and Y s defned as Sm( X, Y) 1. n If 1 l for each and f and 1 1 If 1 l for each and x x LCSS measure: LCS measure can avod the smlar ssues whch brought by the tme seres of short-term mutaton or ntermttent. However, the tme seres of ampltude translaton, tmelne stretchng and bendng deformaton can t get a good smlarty measure results. LCSS measure s desgned for the mprovement of the above problems. Let 0 be an nteger constant, 0 1 a real constant. And f L, L a lnear functon set. Gven two sequences X { x, x,..., x n and Y { y, y,..., y n, let X' { x, x,..., x and Y' { y, y,..., y be the longest 1 2 l 1 2 l subsequences n X and Y respectvely such that: For 1 l, and 1 1 For1 l, 1 l, y /(1 ) f( x ) y (1 ) the sequence wll undetect the canddate seres (Keogh and Pazzan, 2001). Thus, the LCSS algorthm tmelne stretchng support s very lmted. For the exstence problem of LCSS measure, ths study presents an Extended Longest Common Subsequence (ELCS) measure: Let 1and 0 be a real constant, Gven two sequences X { x, x,..., x n and Y { y, y,..., y n, The alzaton that all sequence s located n between value [0,1], Get X { x', x',..., x' al m Y { y', y',..., y' al m,let X' { x', x',..., x' al l and Y ' { y', ',..., ' y 1 y 2 al l be the longest subsequences n X and Y respectvely such that: For1 l, 1 and 1 For1 l, 1 and 1 m n For1 l, x' y' l l Let S ( X, Y ) 2l, al al m n Let S ( X, Y) l f,, n. Then smlarty between the tme seres s defned as formula (1): EXTENDED LONGEST COMMON SUBSEQUENCE MEASURE (ELCS) (1) Although the LCSS measure has some advantages, there are stll the followng ssues:, max, Sm X Y S X Y f L f,, Then the smlarty between the tme seres defned as the formula (2): Sm X, Y max{ S X, Y LCSS measure derved from a soluton set, for dfferent tme seres data set, the selecton of lnear functon f wll dfferent. In other words, only through the tranng data set for the correspondng lnear functon n advance, to further more accurate measure of the smlarty of the sequence. Tranng and test set s always dfferent, so the result s less X mn than deal. x ò 1, m X The LCSS can be appled wth two dfferent length max x mn x ò 1, m ò 1, m (3) sequence comparson, but because of, length dfference of tme seres X { x, x,..., x Whch avod the lnear functon f selecton n of dffcultes, at the same tme retaned the sequence of and Y { y, y,..., y n, that s mn. Otherwse, numercal trend nformaton. 1727, (2) Defned above, parameter maes the search path of the smlarty measure matrx concentrated n a damond area, not only to prevent the sequence of over match, whle reducng the tme complexty. And the selecton of the search path area s related to each sequence length closely, not only appear undetected sequence, but also well adapted tmelne stretchng and deformaton of the sequence match. Parameter θ n the defnton maes the smlarty measurement algorthm, after alzaton, get further flexblty to match the space. Sequence alzed processng as the formula (3):
3 EXPERIMENT Smlarty measure s other data mnng process foundaton, the measure veracty drectly affect other process treatment results. Instead, we can use the clusterng results to estmate the accuracy of the dfferent smlarty measure. Res. J. Appl. Sc. Eng. Technol., 5(5): , 2013 Expermental envronment and the data: The expermental envronment s 2.20 GHz E4500CPU, memory for the 1024M and Wndow XP Professonal system. The expermental data sets use Synthetc Control Chart Tme Seres (SCC) n the UCI of KDD Archve and CBF dataset. The number of expermental data n the SCC s 600, every tme the sequence's length s 60, dvded nto sx categores. The CBF dataset contans Cylnder (C), Bell (B), Funnel (F), t s typcal of synthetc data sets. Fg. 1: Successful classfcaton rate Experment process: In cluster analyss, tme seres of the same group resemble each other, dfferent sets of tme seres are not smlar. Ths study uses the bottomup herarchcal clusterng. Set the ntal data for the C, C,..., C n, the algorthm steps are: Step 1: Each tme seres as a class C Step 2: Calculate the smlarty between any two categores, get a smlarty matrx Step 3: Merge the two categores whch are smlar, then go to Step 2 loop, untl the class number s equal to the predetermned number of clusters The dstance between the clusters uses ELCS smlarty measure computaton. The results of the clusterng are standard,,..., and the clusterng results of each measure C C C C are C ' C ', C ',..., C ', the clusterng accuracy s computed by the followng formula (4) and (5): C C' SmC, C ' 2 C C ' max Sm( C, ' ), ' C Sm C C (4) (5) The calculaton of Sm( C', C ) and same. Because Sm( C', C ) and Sm( C', C) Sm( C, C' ) so s used as a fnal evaluaton 2 crtera. Sm( C, C ') s Sm( C, C ') s asymmetry, 1728 Fg. 2: Average nternal class dstance EXPERIMENTAL RESULTS AND ANALYSIS Parameter determnaton: The experment usng the SCC dataset s to analyses the nfluence of the algorthm. The ELCS measure contans the parameters and θ, the n the performance of the algorthm s very sgnfcant. Wth the changes of the parameter, the clusterng accuracy rate s showed n Fg. 1, the clusterng average nternal class dstance and average among class dstance are shown n Fg. 2 and 3. Wth the ncreases, the clusterng accuracy rate s changed from low to hgh. When 2.2, clusterng accuracy rate s the hghest, the average nternal class dstance s the smallest; the average among class dstance s largest. Ths result means each one of ELCS measure n the sequence satsfes the length. Whle m n s too large, not well qualfed the poston of the test sequence corresponds to the nformaton, get meanngless smlar sequence segments; Whle s too small, the search range of the smlarty matrx s
4 Res. J. Appl. Sc. Eng. Technol., 5(5) : , 2013 Fg. 3: Average among class dstance Fg. 6: Average nternal class dstance for the three dstance Fg. 4: Tme-consumng comparsons of three dstance Fg. 7: Average among class dstance for the three dstance ELCS three nds of dstance tme consumng, set them as the smlarty metrc of herarchcal clusterng. LCSS and the ELCS algorthm s selected the approprate parameters, mang the fnal classfcaton accuracy s ther best. The results are shown n Fg. 4. DTW algorthm consumng sgnfcantly hgher than other, as ELCS measure s condton 1 a and 1 m n s complex than LCSSS measure s, so spend more tme. Fgure 5 s a comparson of DTW, LCSS and the ELCS measure of clusterng accuracy rate. Each measure for the SCC data set has good results, because of the obvous characterstcs of SCCC dataset of data and Fg. 5: Clusterng accuracy rate for the threee dstance the data has a lttle nose. CBF dataset s a randomly generated dataset; each tme seres has a lot of gltches very lmted, a lot of data s dscarded to be mssed. that ncrease the dffculty of the clusterng. But no Wth the decrease of, the classfcaton accuracy matter to whch dataset, ELCS have shown good results, dropped sharply. that s the correct rate of clusterng s the hghest. The dataset dfferences above-mentoned, can be Three nds of measure-based clusterng seen n Fg. 6 and 7 easly. Clusterng results of the CBF comparson: To comparson of DTW, LCSS and the dataset average dstance nternal class s greater than the 1729
5 Res. J. Appl. Sc. Eng. Technol., 5(5): , 2013 SCC dataset, whle the average among class dstance s smaller. Due to LCSS and ELCS are based on LCS algorthm, do not exst DTW algorthm pont corresponds to a mult-pont problems, local nose can be gnored. CONCLUSION Based on the LCS measure, by ntroducng parameters whch standardzes smlarty matrx search path, ths study mproves the accuracy of the smlarty measure and overcomes the tradtonal smlarty measure based on Eucldean dstance whch lac of dealng wth nose nterference. By the experment on two dfferent types of data sets, ELCS measure gets hgher clusterng correctness than the exstng smlarty, but the tme expense s hgher. In short, the measure can be appled effectvely to a varety of tme seres smlarty measure. ACKNOWLEDGMENT Ths study was supported by Doctoral Program Foundaton of Mnstry of Educaton of Chna ( ) and Fundamental Research Funds for the Central Unverstes (2011QNB23). REFERENCES Agrawal, R., C. Faloustos and A. Swam, Effcent smlarty search n sequence database [c]. Proceedngs of 4th Internatonal Conference on Foundatons of Data Organzaton and Algorthms. Sprnger, Berln, pp: Berndt, D. and J. Clfford, Usng Dynamc Tme Warpng to Fnd Patterns n Tme Seres. AAAI-94 Worshop on Knowledge Dscovery n Databases, AAAI Press, Seattle, Washngton. Bollobas, B., G. Das, D. Gunopulos and H. Mannla, Tme-seres smlarty problems and wellseparated geometrc sets [A]. Proceedngs of the 13th Annual Symposum on Computatonal Geometry [C]. ACM Press, New Yor, pp: Chung, L., T.C. Fu and R. Lu, An evolutonary approach to pattern-based tme seres segmentaton. IEEE T. Evolut. Comput., 8(5): Faloutsos, C., M. Ranganathan and Y. Manolopoulos, Fast subsequence matchng n tme-seres databases [J]. SIGMOD Rec., 23(2): Keogh, E.J. and M.J. Pazzan, Dervatve Dynamc Tme Warpng [DB/OL]. Retreved from: &rep=rep1&type=pdf. Km, S.W., S. Par and W. Chu, An ndex-based approach for smlarty search supportng tme warpng n large sequence databases [A]. Proceedngs of the Internatonal Conference on Data Engneerng [C]. IEEE Computer Socety, Hedelberg, pp: Latec L.J., V. Megalooonomou, Q. Wang, R. Laaemper, C.A. Ratanamahatana, et al., Partal elastc matchng of tme seres [A]. 5th IEEE Internatonal Conference on Data Mnng [C]. Nov , Phladelpha. Paterson, M. and V. Danc, Longest common subsequences [J]. Lect. Notes Compu. Sc., 841: Y, B.K., H.V. Jagadsh and C. Faloutsos, Effcent retreval of smlar tme sequences under tme warpng [A]. Proceedngs of the Internatonal Conference on Data Engneerng [C], IEEE Computer Socety, Orlando, pp:
Cluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationA Fast Visual Tracking Algorithm Based on Circle Pixels Matching
A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationBoundary-Based Time Series Sorting
JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA, VOL. 6, NO. 3, SEPTEMBER 2008 323 Boundary-Based Tme Seres Sortng Jun-Ku L, Yuan-Zhen Wang, and Ha-Bo L Abstract In many applcatons, t s desrable
More informationLearning-Based Top-N Selection Query Evaluation over Relational Databases
Learnng-Based Top-N Selecton Query Evaluaton over Relatonal Databases Lang Zhu *, Wey Meng ** * School of Mathematcs and Computer Scence, Hebe Unversty, Baodng, Hebe 071002, Chna, zhu@mal.hbu.edu.cn **
More informationContent Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth
More informationAvailable online at Available online at Advanced in Control Engineering and Information Science
Avalable onlne at wwwscencedrectcom Avalable onlne at wwwscencedrectcom Proceda Proceda Engneerng Engneerng 00 (2011) 15000 000 (2011) 1642 1646 Proceda Engneerng wwwelsevercom/locate/proceda Advanced
More informationBIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING
An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College
More informationSubspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;
Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features
More informationA Deflected Grid-based Algorithm for Clustering Analysis
A Deflected Grd-based Algorthm for Clusterng Analyss NANCY P. LIN, CHUNG-I CHANG, HAO-EN CHUEH, HUNG-JEN CHEN, WEI-HUA HAO Department of Computer Scence and Informaton Engneerng Tamkang Unversty 5 Yng-chuan
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationModule Management Tool in Software Development Organizations
Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationModular PCA Face Recognition Based on Weighted Average
odern Appled Scence odular PCA Face Recognton Based on Weghted Average Chengmao Han (Correspondng author) Department of athematcs, Lny Normal Unversty Lny 76005, Chna E-mal: hanchengmao@163.com Abstract
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationAn Image Fusion Approach Based on Segmentation Region
Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationMining User Similarity Using Spatial-temporal Intersection
www.ijcsi.org 215 Mnng User Smlarty Usng Spatal-temporal Intersecton Ymn Wang 1, Rumn Hu 1, Wenhua Huang 1 and Jun Chen 1 1 Natonal Engneerng Research Center for Multmeda Software, School of Computer,
More informationClustering Algorithm of Similarity Segmentation based on Point Sorting
Internatonal onference on Logstcs Engneerng, Management and omputer Scence (LEMS 2015) lusterng Algorthm of Smlarty Segmentaton based on Pont Sortng Hanbng L, Yan Wang*, Lan Huang, Mngda L, Yng Sun, Hanyuan
More informationNetwork Intrusion Detection Based on PSO-SVM
TELKOMNIKA Indonesan Journal of Electrcal Engneerng Vol.1, No., February 014, pp. 150 ~ 1508 DOI: http://dx.do.org/10.11591/telkomnka.v1.386 150 Network Intruson Detecton Based on PSO-SVM Changsheng Xang*
More informationThe Shortest Path of Touring Lines given in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He
More informationMachine Learning: Algorithms and Applications
14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of
More informationMULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION
MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and
More informationImprovement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration
Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationChinese Word Segmentation based on the Improved Particle Swarm Optimization Neural Networks
Chnese Word Segmentaton based on the Improved Partcle Swarm Optmzaton Neural Networks Ja He Computatonal Intellgence Laboratory School of Computer Scence and Engneerng, UESTC Chengdu, Chna Department of
More informationProfessional competences training path for an e-commerce major, based on the ISM method
World Transactons on Engneerng and Technology Educaton Vol.14, No.4, 2016 2016 WIETE Professonal competences tranng path for an e-commerce maor, based on the ISM method Ru Wang, Pn Peng, L-gang Lu & Lng
More informationQuality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation
Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on
More informationFINDING IMPORTANT NODES IN SOCIAL NETWORKS BASED ON MODIFIED PAGERANK
FINDING IMPORTANT NODES IN SOCIAL NETWORKS BASED ON MODIFIED PAGERANK L-qng Qu, Yong-quan Lang 2, Jng-Chen 3, 2 College of Informaton Scence and Technology, Shandong Unversty of Scence and Technology,
More informationA Simple Methodology for Database Clustering. Hao Tang 12 Guangdong University of Technology, Guangdong, , China
for Database Clusterng Guangdong Unversty of Technology, Guangdong, 0503, Chna E-mal: 6085@qq.com Me Zhang Guangdong Unversty of Technology, Guangdong, 0503, Chna E-mal:64605455@qq.com Database clusterng
More informationA Clustering Algorithm for Key Frame Extraction Based on Density Peak
Journal of Computer and Communcatons, 2018, 6, 118-128 http://www.scrp.org/ournal/cc ISSN Onlne: 2327-5227 ISSN Prnt: 2327-5219 A Clusterng Algorthm for Key Frame Extracton Based on Densty Peak Hong Zhao
More informationStudy of Data Stream Clustering Based on Bio-inspired Model
, pp.412-418 http://dx.do.org/10.14257/astl.2014.53.86 Study of Data Stream lusterng Based on Bo-nspred Model Yngme L, Mn L, Jngbo Shao, Gaoyang Wang ollege of omputer Scence and Informaton Engneerng,
More informationAn Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices
Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal
More informationRecommended Items Rating Prediction based on RBF Neural Network Optimized by PSO Algorithm
Recommended Items Ratng Predcton based on RBF Neural Network Optmzed by PSO Algorthm Chengfang Tan, Cayn Wang, Yuln L and Xx Q Abstract In order to mtgate the data sparsty and cold-start problems of recommendaton
More informationOutlier Detection Methodologies Overview
Outler Detecton Methodologes Overvew Mohd. Noor Md. Sap Department of Computer and Informaton Systems Faculty of Computer Scence and Informaton Systems Unverst Teknolog Malaysa 81310 Skuda, Johor Bahru,
More informationCorrelative features for the classification of textural images
Correlatve features for the classfcaton of textural mages M A Turkova 1 and A V Gadel 1, 1 Samara Natonal Research Unversty, Moskovskoe Shosse 34, Samara, Russa, 443086 Image Processng Systems Insttute
More informationTsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance
Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for
More informationThe Theory and Application of an Adaptive Moving Least. Squares for Non-uniform Samples
Xanpng Huang, Qng Tan, Janfe Mao, L Jang, Ronghua Lang The Theory and Applcaton of an Adaptve Movng Least Squares for Non-unform Samples Xanpng Huang, Qng Tan, Janfe Mao*, L Jang, Ronghua Lang College
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationApproxMGMSP: A Scalable Method of Mining Approximate Multidimensional Sequential Patterns on Distributed System
ApproxMGMSP: A Scalable Method of Mnng Approxmate Multdmensonal Sequental Patterns on Dstrbuted System Changha Zhang, Kongfa Hu, Zhux Chen, Lng Chen Department of Computer Scence and Engneerng, Yangzhou
More informationUnsupervised Learning and Clustering
Unsupervsed Learnng and Clusterng Why consder unlabeled samples?. Collectng and labelng large set of samples s costly Gettng recorded speech s free, labelng s tme consumng 2. Classfer could be desgned
More informationLinkSelector: A Web Mining Approach to. Hyperlink Selection for Web Portals
nkselector: A Web Mnng Approach to Hyperlnk Selecton for Web Portals Xao Fang and Olva R. u Sheng Department of Management Informaton Systems Unversty of Arzona, AZ 8572 {xfang,sheng}@bpa.arzona.edu Submtted
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationTHE PATH PLANNING ALGORITHM AND SIMULATION FOR MOBILE ROBOT
Journal of Theoretcal and Appled Informaton Technology 30 th Aprl 013. Vol. 50 No.3 005-013 JATIT & LLS. All rghts reserved. ISSN: 199-8645 www.jatt.org E-ISSN: 1817-3195 THE PATH PLANNING ALGORITHM AND
More informationFast Computation of Shortest Path for Visiting Segments in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang
More informationA CALCULATION METHOD OF DEEP WEB ENTITIES RECOGNITION
A CALCULATION METHOD OF DEEP WEB ENTITIES RECOGNITION 1 FENG YONG, DANG XIAO-WAN, 3 XU HONG-YAN School of Informaton, Laonng Unversty, Shenyang Laonng E-mal: 1 fyxuhy@163.com, dangxaowan@163.com, 3 xuhongyan_lndx@163.com
More informationMaximum Variance Combined with Adaptive Genetic Algorithm for Infrared Image Segmentation
Internatonal Conference on Logstcs Engneerng, Management and Computer Scence (LEMCS 5) Maxmum Varance Combned wth Adaptve Genetc Algorthm for Infrared Image Segmentaton Huxuan Fu College of Automaton Harbn
More informationHCMX: AN EFFICIENT HYBRID CLUSTERING APPROACH FOR MULTI-VERSION XML DOCUMENTS
HCMX: AN EFFICIENT HYBRID CLUSTERING APPROACH FOR MULTI-VERSION XML DOCUMENTS VIJAY SONAWANE 1, D.RAJESWARA.RAO 2 1 Research Scholar, Department of CSE, K.L.Unversty, Green Felds, Guntur, Andhra Pradesh
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationA Multiresolution Symbolic Representation of Time Series
A Multresoluton Symbolc Representaton of Tme Seres Vasleos Megalookonomou 1 Qang Wang 1 Guo L 1 Chrstos Faloutsos 2 1 Department of Computer & Informaton Scences 2 Department of Computer Scence Temple
More informationA new segmentation algorithm for medical volume image based on K-means clustering
Avalable onlne www.jocpr.com Journal of Chemcal and harmaceutcal Research, 2013, 5(12):113-117 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCRC5 A new segmentaton algorthm for medcal volume mage based
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More informationSuppression for Luminance Difference of Stereo Image-Pair Based on Improved Histogram Equalization
Suppresson for Lumnance Dfference of Stereo Image-Par Based on Improved Hstogram Equalzaton Zhao Llng,, Zheng Yuhu 3, Sun Quansen, Xa Deshen School of Computer Scence and Technology, NJUST, Nanjng, Chna.School
More informationResearch on Categorization of Animation Effect Based on Data Mining
MATEC Web of Conferences 22, 0102 0 ( 2015) DOI: 10.1051/ matecconf/ 2015220102 0 C Owned by the authors, publshed by EDP Scences, 2015 Research on Categorzaton of Anmaton Effect Based on Data Mnng Na
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationA fast algorithm for color image segmentation
Unersty of Wollongong Research Onlne Faculty of Informatcs - Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationA NEW APPROACH FOR SUBWAY TUNNEL DEFORMATION MONITORING: HIGH-RESOLUTION TERRESTRIAL LASER SCANNING
A NEW APPROACH FOR SUBWAY TUNNEL DEFORMATION MONITORING: HIGH-RESOLUTION TERRESTRIAL LASER SCANNING L Jan a, Wan Youchuan a,, Gao Xanjun a a School of Remote Sensng and Informaton Engneerng, Wuhan Unversty,129
More informationBRDPHHC: A Balance RDF Data Partitioning Algorithm based on Hybrid Hierarchical Clustering
015 IEEE 17th Internatonal Conference on Hgh Performance Computng and Communcatons (HPCC), 015 IEEE 7th Internatonal Symposum on Cyberspace Safety and Securty (CSS), and 015 IEEE 1th Internatonal Conf
More informationS1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationAn Improved Image Segmentation Algorithm Based on the Otsu Method
3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,
More information6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour
6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the
More informationFace Recognition Method Based on Within-class Clustering SVM
Face Recognton Method Based on Wthn-class Clusterng SVM Yan Wu, Xao Yao and Yng Xa Department of Computer Scence and Engneerng Tong Unversty Shangha, Chna Abstract - A face recognton method based on Wthn-class
More informationLocal Quaternary Patterns and Feature Local Quaternary Patterns
Local Quaternary Patterns and Feature Local Quaternary Patterns Jayu Gu and Chengjun Lu The Department of Computer Scence, New Jersey Insttute of Technology, Newark, NJ 0102, USA Abstract - Ths paper presents
More informationThe Comparison of Calibration Method of Binocular Stereo Vision System Ke Zhang a *, Zhao Gao b
3rd Internatonal Conference on Materal, Mechancal and Manufacturng Engneerng (IC3ME 2015) The Comparson of Calbraton Method of Bnocular Stereo Vson System Ke Zhang a *, Zhao Gao b College of Engneerng,
More informationLoad Balancing for Hex-Cell Interconnection Network
Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,
More informationTARGET RECOGNITION ALGORITHM BASED ON SALIENT CONTOUR FEATURE SEGMENTS
U.P.B. Sc. Bull., Seres C, Vol. 81, Iss. 1, 2019 ISSN 2286-3540 TARGET RECOGNITION ALGORITHM BASED ON SALIENT CONTOUR FEATURE SEGMENTS Janhu SONG 1, Yungong LI 2, Yanju LIU 3 *, Yang YU 4, Zhe YIN 5 A
More informationHierarchical clustering for gene expression data analysis
Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally
More informationFace Recognition using 3D Directional Corner Points
2014 22nd Internatonal Conference on Pattern Recognton Face Recognton usng 3D Drectonal Corner Ponts Xun Yu, Yongsheng Gao School of Engneerng Grffth Unversty Nathan, QLD, Australa xun.yu@grffthun.edu.au,
More informationActive Contours/Snakes
Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationCan We Beat the Prefix Filtering? An Adaptive Framework for Similarity Join and Search
Can We Beat the Prefx Flterng? An Adaptve Framework for Smlarty Jon and Search Jannan Wang Guolang L Janhua Feng Department of Computer Scence and Technology, Tsnghua Natonal Laboratory for Informaton
More informationFitting: Deformable contours April 26 th, 2018
4/6/08 Fttng: Deformable contours Aprl 6 th, 08 Yong Jae Lee UC Davs Recap so far: Groupng and Fttng Goal: move from array of pxel values (or flter outputs) to a collecton of regons, objects, and shapes.
More informationQuery Clustering Using a Hybrid Query Similarity Measure
Query clusterng usng a hybrd query smlarty measure Fu. L., Goh, D.H., & Foo, S. (2004). WSEAS Transacton on Computers, 3(3), 700-705. Query Clusterng Usng a Hybrd Query Smlarty Measure Ln Fu, Don Hoe-Lan
More information12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification
Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero
More informationScheduling Remote Access to Scientific Instruments in Cyberinfrastructure for Education and Research
Schedulng Remote Access to Scentfc Instruments n Cybernfrastructure for Educaton and Research Je Yn 1, Junwe Cao 2,3,*, Yuexuan Wang 4, Lanchen Lu 1,3 and Cheng Wu 1,3 1 Natonal CIMS Engneerng and Research
More informationUnsupervised Learning
Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS Non-Parametrc Approach Mxture of Denstes Maxmum-Lkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and
More informationFeature-Area Optimization: A Novel SAR Image Registration Method
Feature-Area Optmzaton: A Novel SAR Image Regstraton Method Fuqang Lu, Fukun B, Lang Chen, Hao Sh and We Lu Abstract Ths letter proposes a synthetc aperture radar (SAR) mage regstraton method named Feature-Area
More informationCLUSTERING ALGORITHM OF VEHICLE MOTION TRAJECTORIES IN ENTRANCES AND EXITS OF FREEWAY. Zongyuan SUN1 Dongxue LI2
5th Internatonal Conference on Cvl, Archtectural and Hydraulc Engneerng (ICCAHE 206) CLUSTERING ALGORITHM OF VEHICLE MOTION TRAJECTORIES IN ENTRANCES AND EXITS OF FREEWAY Zongyuan SUN Dongxue LI2 Tongj
More informationHierarchical Image Retrieval by Multi-Feature Fusion
Preprnts (www.preprnts.org) NOT PEER-REVIEWED Posted: 26 Aprl 207 do:0.20944/preprnts20704.074.v Artcle Herarchcal Image Retreval by Mult- Fuson Xaojun Lu, Jaojuan Wang,Yngq Hou, Me Yang, Q Wang* and Xangde
More informationA Multi-step Strategy for Shape Similarity Search In Kamon Image Database
A Mult-step Strategy for Shape Smlarty Search In Kamon Image Database Paul W.H. Kwan, Kazuo Torach 2, Kesuke Kameyama 2, Junbn Gao 3, Nobuyuk Otsu 4 School of Mathematcs, Statstcs and Computer Scence,
More informationSHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE
SHAPE RECOGNITION METHOD BASED ON THE k-nearest NEIGHBOR RULE Dorna Purcaru Faculty of Automaton, Computers and Electroncs Unersty of Craoa 13 Al. I. Cuza Street, Craoa RO-1100 ROMANIA E-mal: dpurcaru@electroncs.uc.ro
More informationPruning Training Corpus to Speedup Text Classification 1
Prunng Tranng Corpus to Speedup Text Classfcaton Jhong Guan and Shugeng Zhou School of Computer Scence, Wuhan Unversty, Wuhan, 430079, Chna hguan@wtusm.edu.cn State Key Lab of Software Engneerng, Wuhan
More informationBioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.
[Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented
More informationA Self-adaptive Similarity-based Fitness Approximation for Evolutionary Optimization
A Self-adaptve Smlarty-based Ftness Approxmaton for Evolutonary Optmzaton Je Tan Dvson of Industral and System Engneerng, Tayuan Unversty of Scence and Technology, Tayuan, 34 Chna College of Informaton
More informationObject-Based Techniques for Image Retrieval
54 Zhang, Gao, & Luo Chapter VII Object-Based Technques for Image Retreval Y. J. Zhang, Tsnghua Unversty, Chna Y. Y. Gao, Tsnghua Unversty, Chna Y. Luo, Tsnghua Unversty, Chna ABSTRACT To overcome the
More informationA Novel Adaptive Descriptor Algorithm for Ternary Pattern Textures
A Novel Adaptve Descrptor Algorthm for Ternary Pattern Textures Fahuan Hu 1,2, Guopng Lu 1 *, Zengwen Dong 1 1.School of Mechancal & Electrcal Engneerng, Nanchang Unversty, Nanchang, 330031, Chna; 2. School
More informationK-means Optimization Clustering Algorithm Based on Hybrid PSO/GA Optimization and CS validity index
Orgnal Artcle Prnt ISSN: 3-6379 Onlne ISSN: 3-595X DOI: 0.7354/jss/07/33 K-means Optmzaton Clusterng Algorthm Based on Hybrd PSO/GA Optmzaton and CS valdty ndex K Jahanbn *, F Rahmanan, H Rezae 3, Y Farhang
More informationAn efficient iterative source routing algorithm
An effcent teratve source routng algorthm Gang Cheng Ye Tan Nrwan Ansar Advanced Networng Lab Department of Electrcal Computer Engneerng New Jersey Insttute of Technology Newar NJ 7 {gc yt Ansar}@ntedu
More informationTPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints
TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process
More informationKent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming
CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems
More informationA Load-balancing and Energy-aware Clustering Algorithm in Wireless Ad-hoc Networks
A Load-balancng and Energy-aware Clusterng Algorthm n Wreless Ad-hoc Networks Wang Jn, Shu Le, Jnsung Cho, Young-Koo Lee, Sungyoung Lee, Yonl Zhong Department of Computer Engneerng Kyung Hee Unversty,
More informationFAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks
2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: 978-1-60595-522-3 FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng
More informationPerformance Assessment and Fault Diagnosis for Hydraulic Pump Based on WPT and SOM
Performance Assessment and Fault Dagnoss for Hydraulc Pump Based on WPT and SOM Be Jkun, Lu Chen and Wang Zl PERFORMANCE ASSESSMENT AND FAULT DIAGNOSIS FOR HYDRAULIC PUMP BASED ON WPT AND SOM. Be Jkun,
More informationCombining The Global and Partial Information for Distance-Based Time Series Classification and Clustering
Paper: Combnng The Global and Partal Informaton for Dstance-Based Tme Seres Hu Zhang, Tu Bao Ho, Mao-Song Ln, and We Huang School of Knowledge Scence, Japan Advanced Insttute of Scence and Technology,
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationAn Internal Clustering Validation Index for Boolean Data
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 6 Specal ssue wth selecton of extended papers from 6th Internatonal Conference on Logstc, Informatcs and Servce Scence
More information