Discovering similar user navigation behavior in Web log data

Transcription

1 Dscoverng smlar user navgaton behavor n Web log data Tawfq A. Al-asad 1 and Ahmed J. Obad 2 1 Department of Computer Scence, Drector, College of IT, Babylon Unversty, Iraq. 2 Department of Computer Scence, Faculty of Educaton, KUFA Unversty, Iraq. Abstarct Wth the growth of World Wde Web and large number Hosts are jon contnuously to the nternet, huge number of access events to Web stes pages were recorded by Servers n log fles, many users share, send, post and download lot of thngs from Web Stes, ths manner can be dffcult to many organzaton and Agents n order to montor and control that, the recorded nformaton and type of analyss used to extract useful knowldege and understandng t become a practcal challenges to many researchers. Log fles can provded many events nformaton regard to Clents actvtes, server actvtes and so on. Many organzaton employee many log fles analyss tools to predct, analyss and montor users behavor towards ste contents.in ths paper we proposed algorthms to analyss hdden nformaton contents n Log fles and dscoverng patterns by dentfed users along wth them navgaton behavors then clusterng smlar users based on dfferent nterestng log fle content for many Web stes that hosted n Web server. Fnd statstcs for every part n log fle command lne whch are not present n many log fles analyss tools are supported here and fnally dscoverng frequent Web stes-users and user's actvtes towards those Web stes Keywords: Web Usage Mnng ; pattern mnng; Clusterng; Log fle analyss; Web log data. INTRODUCTION Informaton on nternet and especally on Web stes ncreasng rapdly day by day, Web Stes play an mportant role n ths manner where authentcated users, users are always uploads, downloads, browsed many contents accordng to them needs and nterest. Web Server provde a way to browse Web Stes by assgnng an IP address or DNS to dentfy t n addton hosted n t, Server record every events n the form of log fle. The process of dscoverng hdden nformaton from Web log fle s called Web Mnng. The am of t s to obtan nformaton about navgatonal behavor and retreve useful nformaton from very large raw data, can be represented by several mllons of event records n log fle. Web log data contans dfferent knds of nformaton and ncludng web document, web structure and user profles. Web mnng classfed nto three categores depend on whch part of Web to be mned [1, 2]. Categores are Web Structure Mnng, Web Content Mnng and Web Usage Mnng, Fgure 1 llustrate the categores of Web Mnng algorthms. Web Structure Mnng s the task for dscoverng knowledge from the structure of hyperlnks wthn Web pages and gven useful nformaton for the relatonshp among Web pages [3, 4]. The clusterng process can play mportant role here by groupng the Web pages based on ther structure, pages can represented by nodes and ther lnks as edges among these nodes, clusterng process can be done here based on graph representaton and understandng structure of Web pages and ts related to other pages n other Web Ste Pages. Lnk structure n Web pages can be classfed nto two types: Frst, hyperlnks that connect dfferent parts n the same page (Intra). Second, hyperlnks that connect two or more dfferent pages (Inter). The other role can be appled here by dentfy trustworthy pages and ther hub pages for a gven subject. Trustworthy pages contan mportant nformaton and supported by several lnks referred to t that means these pages are hghly referenced. Hub pages contan many lnks to trustworthy pages that can gve a role for clusterng Pages based on trustworthy pages. Web Structure Mnng can be employed to effcently mprove nformaton retreval and document classfcaton tasks [5]. Web Content Mnng s the task of dscoverng dfferent knds of nformaton contents and mprovng effcent mechansms to organze and groupng (clusterng) multmeda content to the search engnes for accessng these contents by usng keywords, categores, related contents etc. Multmeda contents on Web pages are vared such as structured content (.e. XML documents), Sem-structured (.e. HTML pages), Unstructured content (.e. plant text), other related contents Images, Audos, Vdeos whch are added to those pages or lnked to other hosted Stes. Recently there are some challenges appear regard to that n the case of many Web stes were desgned by usng not only HTML language, other Languages and systems were nvted here such as Content Management Systems ( CMS) etc. and the plat texts here are encrypted and stored n an SQL data bases and users events were recorded as vsted artcles and n ths case need to combne web mnng algorthms n case to mnng clusterng and extracted useful nformaton from user behavors and contents related. Web CMS s responsble for storng, control and management data and other component n long-term uses. CMS consst of repostory used to store and preserve varous component and use varous databases to store t. Repostory n CMS contan two categores, the frst one comprses source fles as well as CMS confguraton fles, these fles contan nformaton about type of content, metadata, users and group of users along wth them access data, profles and preferences. The second repostory contan databases where content and fles wll be processed through CMS and nhert 8797

2 databases and Tables that are constructed for recall and process the content [6]. Many researches has been done n Web Content Mnng, ncludng text mnng and ts ssues such as: topc dscovery, assocaton pattern dscovery, Web pages classfcaton and Web document clusterng. Other mportant body of work by dscoverng knowledge from mages n the feld of mage processng. Other research ncludng Latent Semantc Indexng (LSI) whch tes to analyzng structure of elements n document collecton, another mportant role lookng for fnd the poston of words n document for solvng the document categorzaton problems and extractng patterns or rules. Topc detecton and trackng also addressed as Web Content Mnng [7, 8]. Web Usage Mnng s the task to dscoverng the nterestng patterns from Web Usage Data. Interestng patterns nclude nformaton about user access patterns along wth varous types of request have been made by sngle or many users. The am of Web Usage Mnng s to understand the browsng and navgaton through Web pages to enhance many thngs such as: the qualty of commercal servces, allotment Web portals [9] or mprove Web structure and Web Server performance [10]. Web Usage Mnng can be defned as the extracton of useful user patterns from Web server access logs fles based on data mnng technques. Sources of log fles nclude Web server, Clent server, proxy server and applcaton servers [11, 12]. By found more than one source place that store the navgaton patterns and users accesses that make the mnng process more dffcult. The best and relable result can be obtan from the log fle that has all three types of log fle. Web page accesses that were cached n proxy servers or n clent sde does not contan records on server sde. Proxy server provde addtonal nformaton however the requested page are mssng n the clent sde, that lead to problem for collectng nformaton from clent sde. Most of Web mnng algorthms work based on Server sde log data, commonly used mnng algorthms are assocaton rule mnng, sequence mnng, clusterng [13]. Web Data Web Mnng Web Structure Mnng Web Content Mnng Web Usage Mnng Lnks Structure Web Search content Pre-processng Internal structure URL mnng Search page content Result page content Pattern Dscovery Pattern Analyss Fgure 1. Web Mnng Categores The organzaton of the paper s as follow: secton 2 llustrate the related work, secton 3 dscuss the log fle types, format and parameters, secton 4 show the Web mnng phases and preprocessng steps for our log fle, secton 5 contan the proposed model and algorthm, secton 6 our analyss result and fnally concluson for our works. RELATED WORK In the feld of Web usage mnng there are several data mnng technques have been used n order to dscover nterestng knowledge based on lookng and focused approach. The nformaton ganed by these technques can be used n many areas such as reconstructng Web stes, predcton next vsted pages, group smlar users, recommendaton systems and so on. Clusterng s the data mnng process that group together smlar tems havng smlar propertes. The clusterng may nclude group of smlar users, pages, references stes etc. Dscoverng group of smlar users n user communtes have been dscussed n [20]. Whle n [23, 24, 25, and 26] authors used Assocaton rule mnng for dscoverng Web pages 8798

3 accessed drectly by other pages. Web Usage Mnng s presented n many approaches along wth applyng data mnng technques for dscoverng nformaton. In [27] where Assocaton rule used for dscoverng relaton among pages, also used for detect the assocaton among group of users wth partcular nterest. Frequent path traversal and patterns topology of paths used wth WAP tree for representng and savng effcent patterns, others such as n [28, 29 and 30] they used Web usage mnng and Meta data for dscoverng terrorst and attacks Web stes. Your paper's fgs must be wthout background fll color, no border fg and no border legend, no vertcal lne, no horzontal Server Log fle types Web server log fles are plan text fles and ndependent from the server, generally there are four types of server logs based on types of nformaton recorded whch llustrate n Table 1 : Access log fle Agent log fle Error log fle Referrer log fle SERVER LOG FILE ANALYSIS Table 1. Format Types of Web server log fles Log fle types Actons Format Extracted knowledge Access log fle 1. Records all users request processed by server. 2. Record nformaton about [Wed Oct 11 14:32: ] [error] [Clent ] clent dened by server confguraton: Users' profles. Frequent patterns. Bandwdth usage. users. /export/home/lve/ap/htdocs/test. Agent log fle 1. User browsers. 2. Browsers verson. "Mozlla/4.0 (compatble; MSIE 4.01; Wndows NT)" Agent verson. Operatng system used. Error log fle Referrer log fle Lst of errors for users request made by server. 1.Informaton about lnk. 2.Redrects vstor to Ste. [Wed Oct 11 14:32: ] [error] [clent ] clent dened by server confguraton: /export/home/lve/ap/htdocs/test " "/page.html" Types of errors. Generated errors IP address. Date and tme of error occurred. Browser used. Keywords. Redrect lnk content. Server Log Fle Format There are three types of log fle format as follow: Common log fle format Is used by most of the web servers. The format of ths log fle s standardzed and can be analyzed by web analyss program, the sample format of ths type s shown below user-dentfer frank [10/Oct/2000:13:55: ] "GET /apache_pb.gf HTTP/1.0" Combned log fle format Is same as common log fle format but there are addtonal nformaton present here, these nformaton are "referral part, user-agent part and cooke prt", the sample format of ths type as bellow user-dentfer frank [10/Oct/2000:13:55: ] "GET /apache_pb.gf HTTP/1.0" Multple access logs Is consder the combnaton of the prevous two types (common log and combned log) fle format, n ths type of log fle format multple drectores can be can be created for access logs, the sample format of ths type as shown below. LogFormat "%h %l %u %t \"%r\" %>s %b" common CustomLog logs/access_log common CustomLog logs/referer_log "%{Referer} -> %U" CustomLog logs/agent_log "%{User-agent}" Server Log Fle Parameters Log fles contan varous parameters and can be very useful for recognzed users browsng attrbutes, many attrbutes can be added or enabled depend on server confguraton and user prvacy agreement, some of cookes and prvate nformaton can be used but n general there are common parameters an be found n log fles. Below wll llustrate n TABLE 2, the lst of some parameters useful for analyss processes. Table 2. Parameters of log fle T Parameter name Descrpton 1 User name (IP address) Identfy users Who vsted Webste by ts IP address. 2 Tme stamp Date and tme when user browsed and spend tme. 3 Request Exact request lne by user 8799

4 4 Status code Code sent by server after each user request 5 Bytes Content length of document transferred 6 User agent The browser that user used to send request 7 Request type Method used by user to send request GET, POST There are several works have been done on log fles each work deal wth partcular ssue of mnng and task, ths paper focus on dentfyng users and then extract knowledge about user behavors to groupng smlar users based on them browsng actvtes, our contrbuton here n case to analyss log fle we select KUFA unversty Apache HTTP server verson 1.1 man web server log fle, as we menton above ths log fle ts standardze text fle format and we are applyng text mnng technques to tokenze and extract nterestng nformaton from that log fle. PHASES OF WEB USAGE MINING In order to extract knowledge from log fle, several problems exst when extract useful nformaton from that log fle and also there are many outler records need to be elmnate from t n ths case we are applyng general phases of Web Usage Mnng to analyss and understand the extracted and vald nformaton. The general phases of Web Usage Mnng as follow: 4 4XX 5 5XX 303 SEE OTHER 304 NOT MODIFIED 400 BAD REQUEST 401 AUTHORIZATION REQUIRED 402 PAYMENT REQUIRED 404 NOT FOUND 500 INTERNAL SERVER ERROR 501 METHOD NOT IMPLEMENTED 502 BAD GATEWAY 503 SERVICE UNAVAILABLE 504 GATEWAY TIME OUT Status code show the success and falures users request, records wth status code less than 200 and greater than 299 are consdered falure records and elmnated from log fle entres. Data cleanng also nclude elmnated records that browsed rrelevant paths such as CSS content, man ste paths, gf, cons and maps etc. by checked suffx part of URL. FIGURE 2 represent porton of KUFA unversty Man Web server (Lnux server) log fle format, n that server DNS were assgned to Host IP address to dentfy Web ste that browsed by several users, We are consder to elmnate the records that browsed Man page due ts common n many records because ts contan lnks to all web stes n our server. Phase 1: Preprocessng Preprocessng phase nclude some actvtes can be appled on log fle for cleanng, dentfyng users, vald URL path and also elmnate outlers from log fle, tasks on preprocessng phase as follow [13]: Data Cleanng log fle contan several records are rrelevant to our work lke redrect path to other Stes, entres belong to top/bottom frames and records contan server error message. Error message dentfed through the status code that has been sent by server when user request partcular content, server status code can be vary and vald status codes are show n table 3. T Code Syntax 1 1XX 2 2XX 3 3XX Table 3. HTTP server status codes Status code Descrpton 100 COUNTINUE 101 SWITCHING PROTOCOL 102 PROCESSING 200 OK 201 CREATED 202 ACCEPTED 203 NON-AUTHORITATIVE INFORMATION 301 MOVED PERMANENTLY 302 FOUND Fgure 2. Porton of Web Server Log fle format The result of ths step produce the vald entres n log fle, next step used to dentfyng unque users and dstngush users that belong to same IP address. The followng algorthm n Fgure 3 used for elmnated rrelevant entres n log fle data. Data Cleanng Algorthm Input: Web Server Log fle data Output: Log fle data Step1: Read log fle record from (Web Server Log Fle). Step2: IF (log Fle Record).URL == (gf, Css, Man.php, ndex.php ) AND (Status code < 200 Status code > 209) 8800

5 Remove from log fle. End IF. Step3: Repeat Step 1 and Step 2 untl EOF (Web Server Log fle). Step4: Stop and Save fle n Data base. END Fgure 3. Data Cleanng Algorthm Steps User Identfcaton Web Usage Mnng does not requred knowledge for user's dentfyng, there s a need to dstngush among dfferent user's behavor. Server logs record of multple sessons for user may vst Web ste frequently. By absent authentcaton mechansms n many Web Server some Web ste used Cookes n Clent-sde, Due to prvacy content ths feature may dsable by users, therefore IP address alone not suffcent to dentfy unque users n general by assgnng many sessons to map IP address [15]. In case of absent user authentcaton and clent-sde cookes the possble accurate user dentfyng method by combnaton IP addresses wth User agent and referrer [13]. The followng fgure (FIGURE 4) show userdentfcaton algorthm steps that used for dentfyng dfferent users from log fle browsng data User Identfcaton Algorthm Input: log fle data Output: Unque Users Table. Step1: Intalzaton Create Table nclude the followng feld: ([User ID, IP's address, Date, Tme, Request, Ste name, User Agent, Sze)]. Step2: Read record from Log fle data Step3: User's IP addresses of tow sequental records are compared. Step4: IF ((IP address) s not n Users Table) THEN Assgn User ID to IP address Add both to Users Table ELSE IF ((IP address) s n User's Table) THEN Check (User Agent f same) then Add t wth Same User ID ELSE Assgn (next User ID) to IP address Add both to Users Table Step5: Repeat Step2-5 untl EOF (log fle data) Step6: STOP, Store Result. END. Fgure 4. User-Identfcaton Algorthm steps Phase 2: Mnng Phase Many technques can be appled here after preprocessng phase to extract knowledge such as assocaton rule mnng, frequent pattern mnng, Classfcaton, Clusterng etc. Dscoverng and analyss users patterns We are focus on clusterng technque n case to extract knowledge about smlar User's behavors based on browsng Web stes characterstcs. Ths technque help n many aspects for understandng smlar user's nterested content and Web stes contents, frequent User's- Ste browsng content, Effects of Ste content to Users and other ndcators related to ths work. Table 4 llustrate nformaton ganed after preprocessng steps, based on ths result we buld approprate Model for applyng clusterng algorthm to group of smlar User's navgaton behavors. Table 4. Identfyng Unque user's navgaton User Id IP address Date Tme Request Ste Name Agent User /Oct/ :01:12 GET AR "Mozlla/5.0 (Phone; CPU Phone OS 8_4) User /Oct/ :01:15 GET journals "Mozlla/5.0 User /Oct/ :01:18 GET AR "Mozlla/5.0 (Wndows NT 6.1) User /Oct/ :01:26 GET journals "Mozlla/5.0 (Wndows NT 6.1) User /Oct/ :01:20 GET conf "Mozlla/5.0 (Phone; CPU Phone OS 8_4) User /Oct/ :01:15 GET journals "Mozlla/5.0 User /Oct/ :01:26 GET AR "Mozlla/5.0 User /Oct/ :45:36 GET Lbr "Mozlla/5.0 User /Oct/ :45:39 GET AR "Mozlla/5.0 User /Oct/ :45:42 GET Lbr "Mozlla/5.0 (Wndows NT 6.1) Usage Data pre-processng result s a set of Μ Web stes vews, W = {W 1, W 2, W 3 W m}, and a set of (Ν) user transactons, T = {t 1, t 2, t 3 t n} where each (t ) s a subset of W. For data mnng tasks such as assocaton rule mnng and clusterng the orderng of Web ste vews s not relevant, we represent each user transactons as a vector over Μ dmensonal space of Web stes vews. In most Web Usage mnng algorthm and collaboratve flterng applcatons 8801

6 weghts were used to construct profles of smlar users. Weghts may be user ratng, spend tme on that page and ether bnary representng the presence or absence of that user from page vew, product vew and Ste vew. In our stuaton we are deal wth ths cases by elmnated the records that vst man page due t's consder the gate for other Web stes lnks and consder the User spent tme for each transacton have been made by a partcular user. Spend tme threshold used here to dstngush the users that browsed Stes for vewng Ste component from others who search for partcular content. Vald user's transactons treated to buld Users-Web stes vst matrx, the followng Table (Table 5) represent the occurrence of users based on vald transactons to construct Users-Web ste vst matrx. Table 5. User-Web ste occurrences vst matrx User Id AR Journals Conf Lbr Art Busn Comm Educ Gelog User User User User User User User User User User User User Due to space of real Table we are show only small part, above Table show for example user1, user2, user4, user7 and user 12 are more nterested n AR Web ste whle user5, user9 and user11 more nterested for thess and readng books from Lbrary Web ste. User Web ste Vst matrx produce many vst fractons for ths purpose we consder the occurrences of all users to be wthn smlar scale. X 0 1 f x T f x T Then new Table 6 after applyng Equaton (1) and (2) represent a user's Web ste vst matrx, clusterng can be appled for the enhanced matrx to fnd groups of smlar users based on browsng and navgaton patterns. Gven the mappng of user transactons nto mult-dmensonal space as enhanced vectors of Web stes vst as n Table 6, standard Herarchal clusterng algorthm can effcent employed here to take the smlarty of groups of users members wth respect to many Web stes vst patterns n the manner to form each possble number of groups n that have smlar behavors. Many clusterng algorthm have been appled here some algorthms consder clck stream to cluster dynamc users behavors by usng Mxture Models, ths process can be too complex to be modeled by usng basc probablty dstrbuton because each user may show dfferent behavors correspond to dfferent tasks, dfferent task reflect dfferent dstrbuton perodcally n such applcaton such as dynamc Web stes. Mxture Markov Models were appled n [17, 18] to cluster users based on smlartes n navgaton behavors. PROPOSED MODEL In order to dscover smlar user navgaton behavor, log data need to be preprocesses, elmnate non-relevant data then applyng data mng technques on result data. When applyng data mnng technques on web data ths s called Web data mng, Web mnng nlcude several tasks based on problem found and nterestng result. Fgure 4 show our proposed model for dscoverng hdden nformaton n Log fle data for varos users actvtes. (2) Cluster analyss and groupng smlar users Many data mnng technques can be appled n ths manner to deal wth fractons of User-Vst matrc, for some data mnng algorthm dfferent range values lead to a tendency and mmoderate nfluence for varables on the fnal result n order to scale the effect of t [16]. Normalzaton work well n ths manner for small values close to (0.0) and hgher ones to (1.0). MIN-MAX Normalzaton one of smplest and most used by scalng the dfference by the range. The MIN-MAX formula s gven as n Equaton (1). x ( x x mn ) ( x x ) max mn (1) Then by applyng threshold the new value updated as n Equaton (2) Fgure 5. Proposed Dagram 8802

7 After applyng equaton (1, 2) for the result Table 6, users wth small number of vstng count were elmnate n each Web ste, users wth hgh vst count pck hgher values and were grouped nto users-web ste nterest. Table 7 show Users-Vst ntersect n each Web ste User Id Table 6. User-Web stes Intersecton matrx AR Journals Conf Lbr Art Busn Comm Educ Gelog User User User User User User User User User User User User above Table 6 show users wth 0 values are (nonnterest/non-vsted) Web stes by correspondng users, whle Web stes wth 1 values refer to users are more nterestng to vst and browsed contentf from those Web stes, from the above result we can nfer for example user1 and user4 are more nterest to Web ste 1and 2, whle user2 and user3 are nterest to Web ste 6 and 7. Incase to fnd the smlartes among users, many bnary smlartes measures can be appled here, n [19] lst smlarty and dstance measures were appled n bnary data. The goal of ths measures s to fnd smlartes among data ponts n our scenaro we are consder ths data as a dynamc because the behavors of users may change through tme and based on them nterest, n ths case we are applyng 2 scenaros as follow : The frst one s by usng Cluster Identfcaton Algorthm (CIA) whch can be vsualze groupng smlar users n our Result Matrx and dentfed smlar users by calculatng the cells ntersecton rato among them, ths process yeld blocks of smlar users that share smlar Web-Stes browsed and elmnated not browsed content Table 6 consder to applyng CIA algorthm. Second scenaro s by usng dstance measure, we are consder each user s a partcular case and ts browsed Webstes dffer from others, n order to fnd smlartes among users so we are arranged Webstes based navgaton orders for example the result n Table 4 and Table 5 are combned together to form a vectors for users, we use character-based coded to represent Web stes names to be smple for comparng, Users wth small hts occurrences were not consdered, user behavors can dscovered through contnuously vsted stes by users, relatve frequency were calculated here for each user, as n Equaton (3), m F ( U ) M T Where users = 1 N, F(U ) relatve frequency for user, m s hts count of user () n partcular Web ste J, M s total number of hts for user (), fnally T selected threshold. Mnmum values are dscarded that does not satsfyng the selected threshold, user's vectors result as follow: (3) Table 7. User-Web Stes Navgaton behavors User AR Journals Conf Lbr Art Busn Comm Educ Gelog Id User User User User User User User User User User User User Then fnd smlartes among users, smlar users are grouped together to form new cluster followng Table 8 show smlarty matrx among users: Table 8. User-Web Stes Navgaton behavors User User User User User User User User User User Use Id r 10 User User User User User User User User User9 0 0 User1 0 0 Jaccard and Bray-Curts for smlarty/dssmlarty measures were appled, from Table 7 many herarchal algorthms can be appled for clusterng smlar users such as Sngle Lnkage and Complete Lnkage the result of Table 7 after applyng clusterng algorthm has ben shown n Table 8. To fnd smlartes among users Equaton (4) used and result compare wth dstance measures used n Equaton (5). 8803

8 a S ( U A, U B) a b c (4) X j X k D ( U A, U B) ( X X ) (5) ID Cluster-name Users j Table 9. Users - Smlarty values 1 Cluster1 user1,user4,user7,user10,user12 2 Cluster2 user2,user3,user6,user9,user10,user11 3 Cluster3/4 user5 /user8 CONCLUSION Ths paper focuse on dscoverng the hdden nformaton from man server general log fle, man server contan combnaton for all Web stes access nformaton that hosted on t n text format, ths fle nclude navgaton actvtes for many Web stes n order to understand the behavors of users towards those stes not for sngle Web ste, the contrbuton of the paper s to extract nformaton from huge log fle and consder novel approaches to deal and analyss users patterns, then extracted useful nformaton for vald sessons after that clusterng approach has been appled to groupng smlar users navgatons behavors, ths can gve as ndcators frequent users nterest towards dfferent Web stes content, montor users actvtes for partcular Web ste, consume bandwdth for each user durng selected perod, montor Web stes vsts and browsed content and many others actvtes for future works. ACKNOWLEDGEMENT Ths work and data analyss result has been supported by Informaton Technology Research and Development Center (ITRDC), Unversty of KUFA, IRAQ and School of Informaton Technology (SIT, Babylon Unversty, IRAQ). REFERENCES [1] Kosala and Blockeel: Web mnng research: A survey, SIGKDD : SIGKDD Exploratons: Newsletter of the Specal Interest Group (SIG) on Knowledge Dscovery and Data Mnng, ACM, Vol. 2, [2] S. K. Madra, S. S. Bhowmck,W. K. Ng, and E.-P. Lm :"Research ssues n web data mnng" n Data Warehousng and Knowledge Dscovery [3] J. Hou and Y. Zhang: Effectvely fndng relevant web pages from lnkage Informaton. IEEE Trans. Knowledge Data Eng., Vol. 15, No. 4, pp , k [4] H. Han and R. Elmasr: Learnng rules for conceptual structure on the Web, J. Intell. Inf. Syst., Vol. 22, No. 3, pp , [5] Renáta Iváncsy, István Vajk: Frequent Pattern Mnng n Web Log Data, Acta Polytechnca Hungarca Vol. 3, No. 1, [6] Danel MICAN, Ncolae TOMAI, Robert Ioan COROŞ: Web Content Management Systems, a Collaboratve Envronment n the Informaton Socety, Informatca Economcă vol.13, no 2/2009. [7] Shqun Yn, Yuhu Qu, Chengwen Zhong, Jfu Zhou: Study of Web Informaton Extracton and Classfcaton Method, Wreless Communcatons, Networkng and Moble Computng, WCom [8] M. A. Bayr, I. H. Toroslu, A. Cosar: A Performance Comparson of Pattern Dscovery Methods on Web Log Data, AICCSA-06, the 4th ACS/IEEE Internatonal Conference on Computer Systems and Applcatons. [9] M. Ernak and M. Vazrganns: Web mnng for web personalzaton, ACM Trans. Inter. Tech., Vol. 3, No. 1, pp. 1-27, [10] J. Pe, J. Han, B. Mortazav-Asl, and H. Zhu: Mnng access patterns effcently from web logs, Proceedngs of the 4th Pacfc-Asa Conference on Knowledge Dscovery and Data Mnng, Current Issues and New Applcatons. London, UK: Sprnger-Verlag, 2000, pp [11] Kohav, R: Mnng e-commerce data: The good, the bad, and the ugly, Proceedngs of the 7th ACM SIGKDD Internatonal Conference on Knowledge Dscovery and Data Mnng, San Francsco, Calforna, 8-13, [12] Anthony Scme: Web mnng: Applcaton and technques, IDEA, chapter 19, pp [13] R. Cooley, B. Mobasher, and J. Srvastava: Data preparaton for mnng world wde web browsng patterns, Knowledge and Informaton Systems, Vol. 1, No. 1, pp. 5-32, [14] L.K. Joshla Grace, V. Maheswar, and Dhnaharan Nagamala: Web Log Data Analyss and Mnng, Proc CCSIT-2011, Sprnger CCIS, Vol 133, pp , Jan [15] Bng Lu: Web data mnng, explorng Hyperlnks, Contents and Usage Data, Second edton, Sprnger, pp , [16] Danel T. Larose, Chantal D. Larose: Data Mnng and predctve analyss, Second edton, WILEY, PP , [17] Cadez, I., D. Heckerman, C. Meek, P. Smyth, S. Whte: Model-based clusterng and vsualzaton of navgaton patterns on a web ste, Data Mnng and Knowledge Dscovery,7(4): p , [18] Ypma, A., T. Heskes: Automatc categorzaton of web pages and user clusterng wth mxtures of hdden Markov models, In Proceedngs of Mnng Web Data for Dscoverng Usage Patterns and Profles,WEBKDD-2002,

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