ANALYSIS AND DATA CLASSIFICATION METHODOLOGY FOR DATA MINING PATTERN

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1 ANALYSIS AND DATA CLASSIFICATION METHODOLOGY FOR DATA MINING PATTERN Dr.M.Prabakaran 1. Mrs.K.Vasuk 2 Mrs.K.Rajeswar 3 1,2,3Assstant Professor. Department of Computer Scence, Govt Arts College,Aryalur *** Abstract - A plethora of bg data applcatons are emergng and beng researched n the computer scence communty whch requre onlne classfcaton and pattern recognton of huge data pools collected from sensor networks, mage and vdeo systems, onlne forum platforms, medcal agences etc. However, as an NP hard ssue data mnng technques are facng wth lots of dffcultes. To deal wth the hardshp, we conduct research on the novel algorthm for data mnng and knowledge dscovery through network entropy. We frstly ntroduce necessary data analyss technques such as support vector machne, neural network and decson tree methods. Later, we analyze the organzatonal structure of network graphcal pattern wth the knowledge of machne learnng methodology and graph theory. Eventually, our modfed method s fnalzed wth decson and valdaton mplementaton. The smulaton results of our approach on dfferent databases show the feasblty and effectveness of our proposed framework. As the fnal part, we provde our concluson and prospect. Key Words: Pattern Analyss and Machne Intellgence, Data Classfcaton Technque, Data Mnng, Knowledge Dscovery, Bg Data and Informaton Securty 1.INTRODUCTION A plethora of bg data applcatons are emergng and beng researched n the computer scence communty whch requre onlne classfcaton and pattern recognton of huge data pools collected from sensor networks, mage and vdeo systems, onlne forum platforms, medcal agences etc. However, as an NP hard ssue data mnng technques are facng wth lots of dffcultes. These dffcultes could be classfed n the followng parts: (1)Pre-processng of the data n the wld. (2) Choce of proper data classfcaton algorthm. (3) The tme-consumng problem. To overcome these draw-backs, we wll analyze the ssue n the paper. The applcatons of data mnng technque vary from subjects to other areas, the most essental applcatons and related researches could be categorzed as the followng parts. (1) Image, vdeo and speech processng. In [1], Anyela et al. conducted research on objectve defnton of rosette shape varaton usng a combned computer vson and data mnng approach. The ppelne provdes a costeffectve and scalable way to the analyss of nherent rose shape change. Image acquston does not requre any specal equpment and a computer program to realze the mage processng and data analyss usng open source software. In [2], Borhan, et al., desgn and mplemented a novel tutorng system based on data classfcaton. In ther research, they propose supervsed machne learnng (SML) models for speech act classfcaton n the context of an onlne collaboratve learnng game envronment. (2) Busness related studes. In [3], Ryan, et al., combne the data mnng technque to the educatonal analyss and related busness model. They ponted out that analyss has become a trend n the past few years, reflected n a large number of graduate program commtment to the analyss of the analytcal sklls of the declaraton of provdng lucratve jobs, and the arport lounge watng to be flled wth advertsements from dfferent consultng company by analyzng the commtment to a bg ncrease n profts. In [4], Stavros, et al., desgned ntellgent E-busness for onlne commerce, the combnaton of fnancal models and data analyss enhance the performance of tradtonal busness. Busness ntellgence s becomng an mportant factor, can help organzatons n the management, development and exchange ther valuable nformaton and knowledge and so on. Data mnng s the man goal of the relatons and the dfferent models, but they exst n the data set between the "hdden" large amounts of data. (3) Medcal Assstance and Auxlary Medcal. Shamsher s group [5] conduct revew on data-guded medcal applcatons, they conclude that all knds of current or potental applcatons of data mnng technque n health nformatcs through some case studes publshed lterature. In(6)Feng, et al., ponted out that medcal nformaton mnng s crucal for dagnoss. Ther revew ndcates that A large number of studes have shown that the real world research has strong external valdty than conventonal randomzed controlled trals, evaluatng the effect of nterventon measures n the actual clncal, open a new path n coronary heart dsease (CHD) comprehensve medcal research. Comprehensve medcal clncal data, however, great n number and complex data types n coronary heart dsease, to explore sutable methodology of a hot topc. Data analyss and knowledge dscovery from the clustered data acts as sgnfcant roles. More related applcatons of data mnng could be found n the followng lteratures [7-15]. In ths paper, we conduct research on pattern analyss and data classfcaton methodology for data mnng and knowledge dscovery. We structure our paper followed by the followng pattern: In the Secton 2, we gve the revew work of pror knowledge on data classfcaton, n the Secton 3, we dscuss our proposed methodology and the Secton 4 gves the expermental analyss and result. In the fnal part, we conclude the paper and set up the prospect. 2018, IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3850

2 2. Pror Knowledge on Data Classfcaton Data and pattern classfcaton s used to classfy each tem n a set of cluster of data nto one of predefned set of groups. Classfcaton s a functon of data mnng, dstrbuted collecton n a project goal category or class. Classfcaton s the purpose of accurately predctng each case of target class data. Classfcaton task begns n a data set of class assgnments are known. Classfcaton of dscrete doesn't mean order. Contnuous, floatng-pont values wll dsplay a numerc value, rather than absolute, and goals. Target usng regresson forecast model and numercal algorthm, s not a classfcaton algorthm. The smplest type of classfcaton problem s bnary classfcaton. In bnary classfcaton, target attrbutes only two possble values: for example, the credt ratng n ether hgh or low credt ratng. In the followng subparts, we analyze the popular mnng algorthms Decson Tree (DT) based Approach Decson tree s a knd of commonly used data mnng methods. Our goal s to create a model, the predcted value of the varable target based on multple nput varables. Each nternal node correspondng to one nput varable, wth every possble nput varable values at the edge of the chldren. Ths s shown n Fgure 1. Each leaf represents a target value of the varable gven the value of the nput varable represents the path from root to leaf. Decson tree nducton algorthm s a recursve functon. Frst of all, should choose a basc attrbutes as condtoned the root node. Then n order to create the most effectve the most accurate tree, the root node must effectvely ntegrate data segmentaton. Each dvson tred to cut a set of nstances, untl they have the same classfcaton. The best dvson s a so-called the nformaton gan. In the followng Fgure 1, we show the basc components of decson tree (DT) through flow chart. Fgure 1. The Basc Component of Decson Tree (DT) We ntroduce the famous C4.5 as an example. C4.5 s an algorthm used to generate a decson tree developed by Ross Qunlan [16-18]. Generaton of decson tree C4.5 can be used for classfcaton, for ths reason, the C4.5 s often referred to as a statstcal classfer. The detaled steps are shown n the Table 1. Table 1. The Pseudo-code of C4.5 Algorthm 2.2. Neural Network (NN) based Approach In more practcal terms neural networks are nonlnear statstcal data modelng tools. There are plenty of researches focused on the neural network theory and methodology [19-22]. They can be used to smulate the complex relatonshp between nput and output or fndng patterns n data. Usng neural network as a tool, a data warehouse companes collect nformaton from the data set s referred to as the process of data mnng. The Fgure 2 shows the structure. Fgure 2. The Basc Structure of Neural Network (NN) 2.3. Support Vector Machne (SVM) based Approach Support Vector Machne (SVM) s supervsed learnng models wth assocated learnng algorthms that analyze data and recognze patterns [23-27]. Support vector machne (SVM) s a set of nput data and predcton, for a gven nput, these two classes n the form of the output, makng t a non - probablstc bnary lnear classfer. Support vector machne (SVM) model functon form smlar to the neural network and radal bass functon, the two popular data mnng technques. However, these algorthms regularzed wellfounded theoretcal method, the bass of support vector machne (SVM). Generalzaton and easy to the qualty of tranng support vector machne (SVM) goes far beyond the ablty of the more tradtonal method. Study of support vector machne tranng algorthm from data classfcaton and regresson rules, for example, you can use the support vector machne (SVM) learnng, radal bass functon (RBF) and polynomal multlayer perceptron classfer. The Fgure 3 shows the basc structure of SVM. The theoretcal analyss of SVM s shown later. 2018, IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3851

3 3. Our Proposed Framework for Data Mnng and Knowledge Dscovery 3.1. Network Formaton Procedure Fgure 3. The Basc Structure of Support Vector Machne (SVM) The target data for classfcaton s denoted as the formula 1. The objectve functon watng to be solved s expressed n the formula Bayesan Network (BN) based Approach Bayesan network known as the tradton data classfcaton algorthm wn great attenton from the research communty. Bayesan networks known as the bayesan network s a drected acyclc graph (DAG) nodes represent random varables of bayesan sense: they may be observed that the number of latent varables, or assumed unknown parameters. Edge sad condtonal dependence; Node connecton represents no condtonally ndependent varables. And the probablty of each node functon s denoted as nput for a partcular node's parent varables and values to varables are expressed as the probablty of the node. The fgure 4 shows the general example of the network. Intally, an undrected and weghted network wth a sngle connected component s bult for each class. In the ntal model, Gl presents the basc class l 1,2,...,L. Therewll be a connecton between two vertces, f they are between the feature vectors of the Eucldean dstance (ED) of less than a predefned threshold. In addton, the connecton should be nversely proportonal to the weght of the vertex dstance the closer, the more must connect. Therefore, the weght parameter between nodes s expressed as the formula 4. In the constructed network, all the nodes are nner-connected under the gudelne of adjacent matrx. If there s a d attrbute n each data tem, s sad to be a dmensonal data set. The second part of the -th tuple characterstcs assocated wth the class label of data tems. The purpose of machne learnng s to create a mappng from x to y, ths mappng s called classfer. It s mportant to note that each class needs to have a network connecton component, so the choce of parameters of must reflect the stuaton. After the step, we successfully buld up our network, whch s also expressed n the formula 5. l G, l 1,2,..., L, 1,2,...,u All of the pror structure of networks wll be adopted n the next phase of the classfcaton process whch wll be dscussed n detal n the next sectons Calculaton of Network Entropy Fgure 4. The Tradtonal Structure of Bayesan Network (BN) Network control theory to the desgn of dstrbuted control strategy, the whole system composed of multple subsystems, each drven by a partcular controller. For example, one possble mplementaton subsystem and the communcaton between the controller dagrams, use problem to desgn the controller, acheve some control or mnmze control cost, at 2018, IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3852

4 the same tme respect these nterconnecton model. Network entropy s the entropy of a stochastc matrx assocated wth the adjacency matrx [28-32]. We thereby set up the defnton of stochastc matrx n the formula 6. Wth ths advanced a new method, we can calculate the network usng the stochastc process of the proporton of pj dynamc entropy descrbes the transton j and t s the statonary dstrbuton of p. Jont optmzaton of the performance of dstrbuted data mnng system, we desgn a dstrbuted onlne learnng algorthm, and ts long-term average reward the best dstrbuted soluton convergence, can get onlne data classfcaton problem gves a complete knowledge of the characterstcs and ther classfcaton functon s appled to the data accuracy and cost. We defne the regret of the dfference between the total expected return best dstrbuted classfcaton scheme s gven full knowledge classfcaton functon of the precson and the expected total return each learners use of the algorthm. We make H p to be the dynamcal entropy. The detaled nducton s defned n the formula 7. H p H, where H pj log pj 3.3. The Regret of Learnng Phase t 1 t 1 Data collected by the dstrbuted processng a set of dstrbuted heterogeneous learners wth the precson of classfcaton functon s unknown. Communcate n ths settng, calculaton and sharng of costs make t s not possble to use the centralzed data mnng technology n a learners can access the entre data set. Wll lmt frst learn a sngle classfer for each vew example of usng the tags. The most confdent on the predctons of each classfer unlabeled data and then use the teraton construct addtonal labeled tranng data. By consderng the dfferent vews of the same data set, the relaton of certan types of data from the predefned vews may be found. Another related techncal commttee machne, t s composed of the classfer of the object. The descrpton s shown n the Fgure 5. Fgure 4. The Descrpton of the Learnng Phase 3.4. The Decson and Valdaton Procedure Fnally, we wll defne the fuzzy classfer C that decdes what class the data tem belongs to. The dea to explan the property of the network to deal wth random changes, so the data tem does not belong to a certan class wll not affect the respectve network. In ths subsecton we defne the regret as a performance measure of the learnng algorthm used by the learners. Smple, t s a pty that lost because of the unknown system dynamcs. I regret that the learners' learnng algorthm defned for me the best k * x learners. The object functon for the phase s defned as the formula 8. R T T T k xt x t E y t y t I d k xt (8) Therefore, the project classfcaton s the mportance of class. The SVN algorthm s a soft clusterng method n whch the objects are assgned to the clusters wth a degree of belef. Therefore, an object can belong to more than one cluster wth dfferent degrees of belef. It tres to fnd the feature ponts n each cluster, named as the center of a cluster, then calculatng the membershp of each object n the cluster. The mathematcal expresson s shown as follows. expresson s shown as follows. C,l l, wher l H Gl 2018, IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3853

5 L k k e H Gl Fgure 5. The Result n the Harvard Database 4. Expermental Analyss and Smulaton In order to verfy the effectveness and feasblty of our proposed methodology, we conduct numercal and expermental smulaton n ths secton. Frstly, we ntroduce the expermental envronment and later, we present three smulatons n order to llustrate the effcency of the proposed hgh level classfcaton method when appled n real and artfcal data sets and compare ts results wth tradtonal classfcaton technques. Fgure 6. The Irs Setosa Sub-part Result 4.1. Envronment of the Experment The smulaton envronment s set up nto the followng condtons. Four physcal machnes (Macbook Pro) wth 4 TB hard dsk and 6 GB of RAM, and the smulaton software s nstalled on Wndows Wn7 platform. The datasets adopted by us vary a lot. The followng dataset are just examples: (1) the Seeds Data Set; (2) database from UCI; Fgure 7. The Verscolour Sub-part Result (3) Irs data set; (4) databased from Cornell Unversty; (5) databased from Harvard Unversty. (4) 4.2. Smulaton through the Harvard datasets Harvard dataset s a database whch contans 3 classes (Irs Setosa, Irs Verscolour, Irs Vrgnca) and 150 nstances, where each class refers to a knd of plant. The Fgure 5 shows our result, we could conclude that our method s robust. The separate results are shown n the Fgure 6-Fgure 8. Fgure 8. The Irs Vrgnca Sub-part Result 4.3. Smulaton through the UCI Datasets UCI database contans 3 classes (Kama, Rosa, Canadan), 210 nstances and 7 attrbutes for each data tem. The 9 shows our result, we could conclude that our method s robust. The separate results are shown n the Fgure 10-Fgure , IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3854

6 Fgure 9. The Result n the UCI Database Fgure 10. The Sub-part Result One B. Same, H. L, F. Keshtkar, V. Rus and A. C. Graesser, Contextbased speech act classfcaton n ntellgent tutorng systems, In Intellgent Tutorng Systems, Sprnger Internatonal Publshng, (2014), pp B. Same, H. L, F. Keshtkar, V. Rus and A. C. Graesser, Contextbased speech act classfcaton n ntellgent tutorng systems, In Intellgent Tutorng Systems, Sprnger Internatonal Publshng, (2014), qq S. Valsamds, I. Kazands, S. Kontoganns and A. Karakos, A Proposed Methodology for E-Busness Intellgence Measurement Usng Data Mnng Technques, In Proceedngs of the 18th Panhellenc Conference on Informatcs, ACM, (2014), pp D. P. Shukla, S. B. Patel and A. K. Sen, A lterature revew n health nformatcs usng data mnng technques, Int. J. Softw. Hardware Res. Eng. IJOURNALS, (2014). Fgure 11. The Sub-part Result Two M. J. Berry and G. Lnoff, Data mnng technques: for marketng, sales, and customer support, John Wley & Sons, Inc., (1997). L. Vaughan and Y. Chen, Data mnng from web search queres: A comparson of google trends and badu ndex, Journal of the Assocaton for Informaton Scence and Technology, vol. 66, no. 1, (2015), rr Fgure 12. The Sub-part Result Three 5. Concluson and Summary A plethora of bg data applcatons are emergng and beng researched n the computer scence communty whch requre onlne classfcaton and pattern recognton of huge data pools collected from sensor networks, mage and vdeo systems, onlne forum platforms, medcal agences etc. However, as an NP hard ssue data mnng technques are facng wth lots of dffcultes. In ths paper, we propose a novel pattern analyss and data classfcaton methodology for data mnng and knowledge dscovery. We adopt the network to modfy the tradtonal methods. The result shows the effectveness of our work, n the future, we plan to do more expermental analyss and mathematcal research for the optmzaton part. References A. Camargo, D. Papadopoulou, Z. Spyropoulou, K. Vlachonasos, J. H. Doonan and A. P. Gay, "Objectve defnton of rosette shape varaton usng a combned computer vson and data mnng approach, PloS one, vol. 9, no. 5, (2014), pp. e N. Sonawane and B. Nandwalkar, Tme Effcent Sentnel Data Mnng usng GPU, In Internatonal Journal of Engneerng Research and Technology, ESRSA Publcatons, vol. 4, no. 02, (2015) February. L. E. Barrera, A. B. Montes-Servín, L. A. Ramírez-Trado, F. Salnas-Parra, J. L. Bañales-Méndez, M. Sandoval-Ríos and Ó. Arreta, Cytokne profle determned by data-mnng analyss set nto clusters of non-small-cell lung cancer patents accordng to prognoss, Annals of Oncology, vol. 26, no. 2, (2015), pp J.-H. Kao, H.-I. Chen, F. La, L.-M. Hsu and H.-T. Law, Decson Tree Approach to Predct Lung Cancer the Data Mnng Technology, In Ubqutous Computng Applcaton and Wreless Sensor, Sprnger Netherlands, (2015), pp S. García, J. Luengo and F. Herrera, Data Sets and Proper Statstcal Analyss of Data Mnng Technques, In Data Preprocessng n Data Mnng, Sprnger Internatonal Publshng, (2015), pp J. Moeyersoms, E. J. de Fortuny, K. Dejaeger, B. Baesens and D. Martens, Comprehensble software fault and effort predcton: A data mnng approach, Journal of Systems and Software, vol. 100, (2015), qq M. Fujmoto, T. Hguch, K. Hosom, M. Takada, L. Manchkant, V. Pampat, R. M. Benyamn, et al., Assocaton between Statn Use and Cancer: Data Mnng of a Spontaneous Reportng 2018, IRJET Impact Factor value: ISO 9001:2008 Certfed Journal Page 3855

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