Design and Implementation of Web Usage Mining Intelligent System in the Field of e-commerce

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1 Available olie at Procedia Egieerig 30 (2012) Iteratioal Coferece o Commuicatio Techology ad System Desig Desig ad Implemetatio of Web Usage Miig Itelliget System i the Field of e-commerce Abstract B.Naveea Devi a, Y.Rama Devi b, B.Padmaja Rai c, R.Rajeshwar Rao d, a* a Departmet of CSE, Mahatma Gadhi Istitute of Techology,Gadipet, Hyderabad, A.P , Idia b Professor, Departmet of CSE, Chaitaya Bharathi Istitute of Techology, Gadipet, Hyderabad, A.P , Idia c Departmet of CSE, Jawaharlal Nehru Techological Uiversity, Hyderabad, A.P.,, Idia d Departmet of CSE, Mahatma Gadhi Istitute of Techology,Gadipet, Hyderabad, A.P , Idia The risig popularity of electroic commerce makes data miig a idispesable techology for several applicatios, especially olie busiess competitiveess. The World Wide Web provides abudat raw data i the form of web access logs. Now a days may busiess applicatios utilizig data miig techiques to extract useful busiess iformatio o the web evolved from web searchig to web miig. This paper itroduces a web usage miig itelliget system to provide taxoomy o user iformatio based o trasactioal data by applyig data miig algorithm, ad also offers a public service which eables direct access of website fuctioalities to the third party. Published by Elsevier Ltd. Selectio ad/or peer-review uder resposibility of ICCTSD Ope access uder CC BY-NC-ND licese. Keywords: Data Miig; Iformatio Retrieval; Ope Web Services; Web Usage Miig, Web Computig. 1. Itroductio The goal of Web Usage Miig is to fid out extract the useful iformatio from web data or web log files. The other goals are to ehace the usability of the web iformatio ad to apply the techology o the web applicatios, for istace, pre-fetchig ad catchig, persoalizatio etc. For decisio maagemet, the result of web usage miig ca be used for target advertisemet, improvig web desig, improvig satisfactio of customer, guidig the strategy decisio of the eterprise ad market aalysis [1]. Recetly there are a large umber of web services that we ca use ad may of them are ope source based. Web services are APIs that facilitate the commuicatio betwee applicatios for example RapidMier, Digg.com, Amazo, ebay are opeed access to their services ad data through APIs, ad we * B. Naveea Devi. Tel.: ; fax: address: veeamgit@yahoo.com Published by Elsevier Ltd. Ope access uder CC BY-NC-ND licese. doi: /j.proeg

2 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) ca make use of their services for the developmet of web usage miig research applicatios. The cocept of Web APIs eables direct access to the website fuctioalities i order to leverage third party efforts o value addig services [2]. However, the umber of compaies, services or web sites that gather iformatio about users icreasig cotiuously. These systems store private iformatio about users ad for that reaso appears much cotroversy about the legitimacy. The mai problem is that these compaies do t share iformatio with the rest of the world. I this paper, we preset a public system to store iformatio about their products ad view details about user behavior. Some of the problems about sharig iformatio would be solved if there was a public service for user behavior iformatio. If all people ca access that iformatio, all of them will have the same opportuities ad will be at the same poit i a commercial eviromet [2]. The rest of the paper is orgaized as various sectios: sectio 2 will have implemeted details about how Hierarchical Agglomerative Clusterig applied o sample web log for mobile marketig. Sectio 3 elaborates how to provide public service (API) which eables third party to view their customer s behavior. Fially Sectio 4 demostrates experimetal result ad Sectio 5 Coclusio with future work. 2. Hierarchical agglomerative clusterig I this paper we focus o, stadard data miig techiques such as clusterig a particular user may associate with other users exhibitig similar behavior patter ad prefereces. Due to the heterogeeity of user s browsig features, the hierarchical agglomerative clusterig algorithm is used to class user s browsig behaviors. Agglomerative hierarchical clusterig starts with every sigle object i a sigle cluster. The, i each successive iteratio, it agglomerates the closest pair of clusters by satisfyig some similarity criteria, util all of the data is i oe cluster. However, it is ecessary to defie a suitable termial coditio whe the agglomerative process should ed [3]. I the hierarchical clusterig, the geeral similarity measures are Euclidea distace fuctio. I the iitializatio, every user is see to be a cluster. The similar users browsig feature will be foud out ad merged ito a cluster util termial coditio is satisfied. Fially, the user clusters will be displayed based o browsig timigs Patter Represetatio We have take sample web log file for mobile marketig as show i table 1 for illustratio purpose. At this poit i time, we assume that user sessios ca be accurately determied. This log file cotais details like user id, ame of the compay, ame of the product, log i time, log out time, compay sessio start time, compay sessio ed time, product sessio start time, product sessio ed time, respective access time i secods. I the task of patter represetatio, user sessios are created from web log files. User sessios ca be reorgaized as a m x k matrix as table 1, each row ca be preseted by Sessio u = (P u,1, P u,2,..p u,k ). The k is the umber of clusters which is ecessary to defie a suitable termial coditio whe the agglomerative should be ed. We have take parameter k value as 3. Oe straightforward approach i creatig a aggregate view of each cluster is to compute the cetroid of each cluster. We have take the dimesio value for each sessio i the mea vector is computed by fidig the ratio of the sum of the sessio weights across trasactios to the total umber of trasactios i the cluster.

3 22 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) Table 1. Sample Web log file UNAME COMPANY PRODUCT LOGIN START LOGIN END COMPANY SESSION START COMPANY SESSION END PRODUCT SESSION START PRODUCT SESSION END krisha lg LG KP500 11:26:30 11:27:04 11:26:42 IST :27:04 IST :26:42 11:27:04 teja samsug Samsug CDMA F679 11:27:55 11:29:03 11:28:52 IST :29:03 IST :28:52 11:29:03 praav okia Nokia AEON 18:59:48 19:09:45 19:00:48 IST 19:09:45 IST 19:00:51 19:09:45 shiva123 samsug samsug :12:09 19:13:20 19:13:03 IST 19:13:20 IST 19:13:07 19:13:20 valee okia Nokia AEON 15:56:38 15:58:04 15:56:47 IST 15:58:04 IST 15:56:51 15:58:04 valee soy samsug :01:58 17:03:32 17:03:27 IST 17:03:32 IST 17:02:49 17:03:32 The similarity betwee ay two users ca be calculated by distace measure. We have take Euclidea distace measure istead of other techiques as the smaller the distace, the more similar the two objects are to each other. Euclidea distace fuctio (1) is used for computig the similarity betwee user i ad user j, the similarity ca be preset by Sim (user i, user j ) = (sessio i, sessio j ). Euclidea distace is further ormalized by equatio (2). Further, the m x m matrix of user similarity will be obtaied. Euclidea distace: D(user i,user j ) = k Normalizatio : ND (user i,user j )= 1- Clusterig : ( p i p (1) k 2, l j, l ) ( p k p 2 i. l j, l ) (2) I the hierarchical agglomerative clusterig method, the distaces are cosidered betwee cetroids of clusters. The two clusters are merged by the shortest distace betwee two cetroids. I the fial, the ew cetroid vector of ew cluster will be calculated by equatio (3). I this paper, the sigle-likage ad complete-likage are ot cosidered, but distaces of cetroids are used. It is assumed there are objects

4 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) i a cluster, the feature of each object ca be represeted by (p i,l, p i,2,..,p i,k ) where 1< i <. The cetroid vector of cluster ca be calculated as follows: Cetroid cluster = p p l,1 l,2 l, k,,..., (3) p (1) Iitializatio cluster: (1.1) Each object be a cluster. (1.2) Creatig similarity matrix of users. (2) Clusterig: (2.1) Fidig a pair of the most similar clusters ad mergig. (2.2) Computig the ew cetroid vector of ew cluster. (2.3) Computig the distaces betwee ew cluster ad others. (2.4) Pruig ad updatig the similarity matrix. (2.5) If the termial coditio is satisfied the output, else repeatig 2.1 to 2.4. (3) Clustered output. Fig. 1. Hierarchical agglomerative clusterig procedure 3. Eablig techologies to provide API for user behavior iformatio Web services are implemeted by a set of core techologies that provide the mechaisms for commuicatio, descriptio, ad discovery of services. The stadards that provide these fuctioalities are simple object access protocol (SOAP), web services descriptio Laguage (WSDL) ad uiversal descriptio, discovery, ad itegratio (UDDI) [4]. These XML based stadards use commo Iteret protocols for the exchage of service requests ad resposes. Fig.2 shows the relatioship of these techologies as a stadards stack for web services. Whe a service provider creates a ew service, it describes the service usig WSDL. WSDL defies a service i terms of the messages to be exchaged betwee services ad how they ca be boud by specifyig the locatio of the service with URL. To make the service available to service cosumers, the service provider registers the service i a UDDI registry by supplyig the details of the locatio of the service provider, the category of the service, ad techical details o how to bid to the service. The UDDI registry will maitai poiters to the WSDL descriptio ad to the service. Whe a service cosumer wats to use a service, it queries the UDDI registry to fid a service that matches its eeds ad obtais the WSDL descriptio of the service, as well as the access poit of the service. The service cosumer uses the WSDL descriptio to costruct a SOAP message to be trasported over HTTP to commuicate with service [4][5].

5 24 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) Service publicatio ad discovery (UDDI) Service descriptio (WSDL) Core web service Stadards Service Commuicatio (SOAP) Packagig (XML) Trasfer ad etwork protocols (HTTP & TCP) Fig. 2. Web service stadard stack. 4. Experimetal results with screeshots This sectio demostrates a simple walk-through of home page approach which cotais the major liks like Home, Logi, ad Registratio for iteractio with applicatio. I user sessio, the browsed pages will be recorded i the log file accordig to trasactioal sequeces. Web usage miig itelliget system retrieves the useful iformatio from web access log which stored at backed, apart from home page there is lik for admiistrator to cotrol the desig of web site by viewig the progress ad feedback of the customers. Fig. 3. Home page for Admiistrator The Fig. 3 displays Admiistrator choice which cotais tabs like Approve compay, view progress, view product result, view feedback to eable the admiistrator to view the status of browsig behavior of customers.

6 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) Table. 2 Output scree for user iformatio Table 2 shows the access log visitig status of user sessio, the browsed iformatio will be recorded i the log file accordig to the trasactioal sequece. This kid of iformatio ca be used to form Access Sequece. By aalyzig the characteristics of these sequeces, we ca better uderstad users browsig habits so as to predict users ext actio ad offer persoalized website cotet ad service based o correspodig forecast. Table 3 Output scree for aggregated timig iformatio

7 26 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) The Table 3 shows the result of aggregated timig iformatio of users based o browsig trasactioal data. This kid of iformatio ca be used to form access sequece after preprocessig. Table 4 Clustered output scree based o Compay The Table 4 shows clustered output after applyig the algorithm. Groupig the compaies based o user iterest depedig o the trasactioal browsig timigs. Fig. 4. Frequecy of browsig timig occurreces of users.

8 B. Naveea Devi et al. / Procedia Egieerig 30 (2012) Fig. 4. shows the fial graph cotaiig details about umber of users verses accessig timigs. Graph cotais details of maximum time utilizatio values, ad miimum time utilizatio ad average time utilizatio values of various users. The aalysis ad visualizatio of time dimesio aggregates trasactio records o daily or weekly basis provides a etrepreeur to take better decisio ad abormality with respect to the time dimesio. 5. Coclusio The importace of web usage miig is uquestioable with the risig importace of the web ot oly as a iformatio portal but also as a busiess edge. Web access logs cotai abudat raw data that ca be mied for web access patters, which i tur ca be applied to improve the overall surfig experiece of users. By takig ito cosideratio we have maily focused o desigig of web usage miig itelliget system for clusterig of user behaviors usig agglomerative clusterig algorithm. Experimets coducted o web logs show the viability of our approach. However, much work is still eeded to add more fuctioality to web miig services, to make web usage miig more useful i the electroic commerce domai. Refereces: [1] Chu-Hui Lee, Yu-Hsiag Fu Web Usage Miig Based o Clusterig of Browsig Features, IEEE Eighth Iteratioal Coferece o Itelliget Systems Desig ad Applicatios, 2008, p [2] Hsichu Che, Xi Li Usig Ope Web APIs i Teachig Web Miig IEEE Trasactios o Educatio, Vol. 54, Issue 4, 2009, p [3] Gago, J.M. Guerrero, C. Juiz, C. Puigjaer, R. Web Miig Service (WMS), a public ad free service for web data miig IEEE Fourth Iteratioal Coferece o Iteret ad Web Applicatios ad Services, 2009, p [4] Richi Nayak Facilitatig ad Improvig the Use of Web Services with Data Miig [5] Xili Zhag, Xiagdog Yi Desig of a Iformatio Itelliget System based o Web Data Miig, IEEE Iteratioal Coferece o Computer Sciece ad Iformatio Techology, 2008, p