IEICE TRANS. INF. & SYST., VOL.E200 D, NO.1 JANUARY

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1 IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 PAPER Specia Issue on Information Processing Technoogy for web utiization A new simiarity measure to understand visitor behavior in a web site Juan VELÁSQUEZ, Nonmember, Hiroshi YASUDA, Terumasa AOKI, Reguar Members, and Richard WEBER, Nonmember SUMMARY The behavior of visitors browsing in a web site offers a ot of information about their requirements and the way they use the respective site. Anayzing such behavior can provide the necessary information in order to improve the web site s structure. The iterature contains aready severa suggestions on how to characterize web site usage and to identify the respective visitor requirements based on custering of visitor sessions. Here we propose to combine visitor behavior with the content of the respective web pages and the simiarity between different page sequences in order to define a simiarity measure between different visits. This simiarity serves as input for custering of visitor sessions. The appication of our approach to a bank s web site and its visitor sessions shows its potentia for internet-based businesses. key words: web mining, browsing behavior, simiarity measure, custering.. Introduction Anayzing visitor browsing behavior in a web site can be the key for improving both, contents and structure of the site and this way assures the institutionas successfu participation in Internet. Web og fies contain information about the visitors interaction with the respective web site. Depending on the traffic, these fies can contain miions of registers with a ot of irreevant information each, such that its anaysis becomes a compex task [7]. Appying web usage mining techniques [2], aows to discover interesting pattern about the visitor behavior. Compemented with semantic web mining, resuts can be improved [4]. Here we propose a new simiarity measure between different visitor sessions based on usage and content of the web site. In particuar, this measure uses the foowing three variabes, which we determine for a visitors: content of each visited web page, time spent Manuscript received May 30, Manuscript revised 0, E-mai:{jveasqu,yasuda,aoki}@mpeg.rcast.u-tokyo.ac.jp, Research Center for Advanced Science and Technoogy, University of Tokyo, 3th Buiding, 4-6- Komaba, Meguro-Ku Tokyo, Japan P.C E-mai: rweber@dii.uchie.c, Department of Industria Engineering, University of Chie, Repúbica 70,Santiago, Chie on it, and the sequence of visited pages. We proved the effectiveness of the proposed simiarity measure appying sef-organizing feature maps (SOFM) for session custering. Any other unsupervised custering method coud be used as we for this purpose. This way, simiar visits are grouped together and typica visitor behavior can be identified, which gives way to improvements of web sites and better understanding of visitor behavior. The specia characteristic of the SOFM is its thoroida topoogy, which has shown its advantages when it comes to maintain the continuity of custers [6] or when the data correspond to a sequence of events, ike e.g. voice patterns [7]. In the case of visitor behavior we have a simiar situation. Section 2 of this paper provides an overview on reated work. In section 3 we describe the data preparation process, which is necessary for comparison of visitor sessions (section 4). In section 5 we show how a sef-organizing feature map was used for session custering using the previousy introduced simiarity measure. Section 6 describes the appication of our work to the case of a Chiean bank. Section 7 concudes this work and points at future work. 2. Reated Work 2. Overview on Web Mining In order to understand the visitor behavior in the web, we wi use web mining techniques. They aim at finding usefu information from the Word Wide Web (WWW). This task is not trivia, considering that the web is a huge coection of heterogeneous, unabeed, distributed, time variant, semi-structured and high dimensiona data [2]. Therefore, a data preparation process is necessary previous to any anaysis. The web mining techniques, can be categorized in three areas: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WUM), see e.g. [4], [2] for a short description. 2.2 Anayzing the Web using custer agorithms The main idea of custering is to identify casses of ob-

2 2 IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 jects (custers) that are homogeneous within each cass and heterogeneous between different casses. Therefore it is necessary to have a measure to determine simiarity between objects. Let Ω be a set of m vectors ω i R n, i =,..., m. The custering goa is to partition Ω in K groups, where C k denotes the k th custer. Then, ω i C means that ω i is more simiar to the eements in this custer than to objects from other custers. Custering requires a simiarity measure, ζ(ω p, ω q ) in order to compare two vectors from Ω and the way to determine the number of custers depends on the method used. In the case of supervised cassification, the vaue K is set a priori. In unsupervised custering, such as e.g. Sef-organizing Feature Maps the custer number is determined by the method itsef. Recent studies [7], [0] [3], [6], underine the benefits of custering for visitor behavior anaysis. 2.3 Web content and usage mining proposition It aims at combining the phiosophies behind WCM and WUM through providing a compemented vision of both techniques [0]. Appying WUM [3] we can understand the visitor browsing behavior, but we cannot discover which content is interesting for the visitor. This anaysis is possibe using WCM [4]. The first step of our suggestion is the definition of a simiarity measure that aows to compare behaviors of different visitors, through the anaysis of visitor preferences. This measure is based on the content of the visited pages, the time a visitor spent on each page and the sequence of pages in his/her session. As a second step we propose to use this measure in a custer agorithm in order to find groups of simiar visitor sessions and using this information, make prediction about the preferences of the future web site s visitors. 3. Data preparation process We propose to use two kinds of data sources that are easiy avaiabe: web og registers and web pages. The web og registers contain information about the browsing behavior of the web site visitors, in particuar the page navigation sequence and the time spent in each page visited. The data from web ogs is based on the structure given by W3C. The second data source is the web site itsef. Each web page is defined by its content. For our simiarity measure we need ony the set of words from each page. In order to study the visitor s behavior, it is necessary to prepare the data from both sources, i.e., web Web ogs are deimited text fies as specified by RFC 266, Hypertext Transfer Protoco HTTP/. ogs as we as web pages, using fiters and identifying the rea user sessions. 3. Web og data preparation process A web og fie contains information on the access of a visitors to a particuar web site in chronoogica order. In a common og fie each access to one of its pages is stored together with the foowing information: IP address and agent. Time stamp. Embedded session Ids. Method. Status. Software Agents. Bytes transmitted. Objects required (page, pictures, etc). Based on such og fies we have to determine for each visitor, the sequence of web pages visited in his/her session. This process is known as sessionization [5]. It considers a maximum time duration given by a parameter, which is usuay 30 minutes in the case of tota session time. Based on this parameter we can identify the transactions that beong to a specific session using tabes and program fiters. Figure shows the transformation sequence for web og registers. This process can be reaized using data streams and program code ike per in unix systems. The first stream contains the og registers grouped by IP and agents, which are separated by a space (see figure ; eft coumn). We ony consider registers whose code is not error and whose URL parameter ink to web page objects. Other objects, such as pictures, sound, inks, etc. are not considered. In the second coumn of figure, the registers are ordered by date, maintaining the groups. Finay, we seect registers from a time window of 30 minutes that are grouped together in sessions in the third stream (third coumn in figure ). IP Agent MSIE MSIE MSIE MSIE MSIE MSIE MSIE 5.5 Date... 6 Jun 02 6:39: Jun 02 6:39:58 6 Jun 02 6:42:03 6 Jun 02 6:24: Jun 02 6:26:05 6 Jun 02 6:42: Jun 02 6:58:03 6 Jun 02 6:32:06 6 Jun 02 6:34:0 6 Jun 02 6:38:40 6 Jun 02 7:34: Jun 02 7:35:45 Fig. IP Agent Date Sess MSIE Jun 02 6:39: MSIE Jun 02 6:39: MSIE Jun 02 6:42: MSIE Jun 02 6:24: MSIE Jun 02 6:26: MSIE Jun 02 6:42: Jun 02 6:32:06 6 Jun 02 6:34:0 6 Jun 02 6:38:40 6 Jun 02 7:34:20 6 Jun 02 7:35: Sessionization process 3.2 Web page content preparation process A web page contains a variety of tags and words that do not have direct reation with the content of the page we want to study. Therefore we have to fiter the text eiminating the foowing types of words:

3 VELÁSQUEZ et a.: A NEW SIMILARITY MEASURE TO UNDERSTAND VISITOR BEHAVIOR IN A WEB SITE 3 HTML Tags. Some tags show interesting information about the page content, for instance, the <tite> tags mark the web page centra theme. In this case, we used this information to identify specia words inside the text. Stop words (e.g. pronouns, prepositions, conjunctions, etc.) Word stemming. Process of suffix remova, to generate word stems [2]. After fitering, we represent a document (web site) by a vector space mode [], in particuar by vectors of words. Let R be the number of different words in a web site and Q be the number of its pages. A vectoria representation of the web site woud then be a matrix M of dimension RxQ with: M = (m ij ) i =,..., R and j =,..., Q() where m ij is the weight of word i in page j. We propose to estimate these weights using equation 2, which is based on the tfxidf-weighting []. m ij = f ij ( + sw(i) T R ) og( R n i ) (2) f ij is the number of occurrences of word i in page j and n i is the tota number of times word i appears in the entire web site. Additionay, we propose to augment word importance if a user searches for a specific word. This is done by sw (specia words), which is an array with dimension R. It contains in component i the number of times that a user searches word i during a given period (e.g.: one month). TR is the tota number of times that a user searches words in the Web site during the same period. If TR is zero, sw(i) T R is defined as zero, i.e. if there has not been any word searching, the weight m ij depends just on the number of occurrences of words Distance measure between two pages With the above definitions we can use vectoria inear agebra in order to define a distance measure between two web pages. Definition (Word Page Vector): WP k = (wp k,..., wp k R ) = (m k,..., m Rk ) k =,...,Q. Based on this definition, we used the ange s cosine as simiarity measure between two page vectors [2]: R dp(w P i, W P j k= ) = wpi k wpj k R R (3) k= (wpi k )2 k= (wpj k )2 We define dp ij = dp(w P i, W P j ) as the simiarity between page i and page j of the web site. 3.3 Navigation sequence preparation process The navigation sequence can be iustrated by a graph G as shown in figure 2, where each edge (web page) is represented by an identification number. Let E(G) be the set of edges in graph G. Figure 2 shows the structure of a simpe web site. If we suppose two visitors visiting the site, the respective sub-graphs can be G = { 2, 2 6, 2 5, 5 8} and G 2 = { 3, 3 6, 3 7}. Then E(G ) = {, 2, 5, 6, 8} and E(G 2 ) = {, 3, 6, 7} with E(G ) = 5 and E(G 2 ) = 4, respectivey. In this exampe, page 4 has not been visited Fig. 2 S S 2 = (,2,6,5,8) =(,3,6,7) A web site with two navigation sequences From these graphs we can determine the visitors navigation sequence S = S(G ) = (, 2, 6, 5, 8) and S 2 = S(G 2 ) = (, 3, 6, 7), respectivey. They represent the pages visited and the navigation sequence Comparing navigation sequences For the simiarity of visits we propose in this paper we need the simiarity of the respective navigation sequences. Therefore it is necessary to define a measure that considers how much two sub-sequences have in common. Equation 4 introduces a simpe way to compare two navigation sequences [4]. E(G ) E(G 2 ) dg(g, G 2 ) = 2 E(G ) + E(G 2 ) (4) Notice that dg [0, ] and if G = G 2, we have dg(g, G 2 ) =. In the case of disjoint graphs, dg(g, G 2 ) = 0 because E(G ) E(G 2 ) = 0. The probem of equation 4 is that its nominator does not take into consideration the sequence of visited pages in each sub-graph; it just ooks at the set of visited pages. If we want to consider how simiar two sequences are, we have to compare aso the respective sequences. i.e., instead of cacuating E(G ) E(G 2 ) we have to determine how simiar two sequences are, considering the order in which the nodes are visited.

4 4 IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 For exampe, both sequences in figure 2 can be represented by a string of tokens [4] such as S = 2658 and S 2 = 367. We need to know how simiar or different are both sequences in its string representation. For this purpose we use the Levenshtein distance [9], aso known as edit distance. It determines the number of transformations necessary to convert S in S 2. This number can be used as dissimiarity measure for the two sequences. For two sequences, ˆx p = (x,..., x p ) and ŷ q = (y,..., y q ), the Levenshtein distance is defined as: p q = 0 q p = 0 L( ˆx p, ŷ p ) = min{l( x p ˆ, ŷ q ) +, ese L( ˆx p, y q ˆ ) +, L( x p ˆ, y q ˆ ) + z(x p, y q )} where z(i, j) = { 0 if i = j otherwise (5) (6) Anayzing the definition of the Levenshtein distance reveas the importance of the order of the token to be compared. For instance, if we compare S and S 2, three transformations wi be necessary. If we have e.g. S 3 = 2856 and S 4 = 367 four transformations wi be necessary. This characteristic makes the Levenshtein distance very suitabe to compare two navigation sequences. L(S, S 2 ) dg(g, G 2 ) = 2 E(G ) + E(G 2 ) (7) Using the exampe in figure 2 and equation 7 eads to dg(g, G 2 ) = Comparing visitor sessions in a web site We propose a mode to represent visitor behavior using three variabes: the sequence of visited pages, their content and the time spent in each one of them. The mode is based on a visitor behavior vector with dimension n and two parameters in each component. Definition 2 (Visitor Behavior Vector): υ = [(p, t )... (p n, t n )], being (p i, t i ) parameters that represent the i th page from a visit and the time spent on it, respectivey. In this expression, p i is the page identifier. For instance, using the browsing navigation shown in figure 2, we have υ = [(, 3), (2, 40), (6, 5), (5, 6), (8, 5)]. Let α, β be two visitor behavior vectors with cardinaity C α and C β respectivey and Γ( ) a function that appied over α or β returns the respective navigation sequence. The proposed simiarity measure is introduced in equation 8 as: sm(α, β) = dg(γ(α), Γ(β)) η η τ k dp(p h α,k, p h β,k) (8) k= where η = min{c α, C β }, and dp(p h α,k, ph β,k ) is the simiarity between the k th page of vector α and the k th page of vector β. The first eement of equation 8 is the sequence simiarity introduced in equation 7. As second eement, τ k = min{ tα k, tβ t β k t α } is indicating k k the visitor s interest in the pages visited. We assume that the time spent on a page is proportiona to the interest the visitor has in its content. If the times by visitors α and β on the k th page visited (t α k, tβ k, respectivey) are cose to each other, the vaue of the expression wi be cose to. In the opposite case, it wi be cose to 0. The third eement, dp, measures the simiarity of the pages visited. It is possibe that two users visit different web pages in the web site, but the content is simiar, e.g., one page contains information about cassic rock and another one about progressive rock. In both cases the users have interest in music, specificay in rock. This is a variation compared to the approach proposed in [7], where ony the user s path was considered but not the content of each page. Finay, we combine in equation 8 the content of the visited pages with the time spent on each of the pages by a mutipication. This way we can distinguish between two users who had visited simiar pages but spent different times on each of them. Simiary we can separate between visitors that spent the same time visiting pages with different content in the web. 5. Sef Organizing Map for Session Custering We used an artificia neura network of the Kohonen type (Sef-organizing Feature Map; SOFM), in order to mine the visitor behavior vectors and discover knowedge [] about the visitor preferences. Schematicay, it is presented as a two-dimensiona array in whose positions the neurons are ocated. Each neuron is constituted by an n-dimensiona vector, whose components are the synaptic weights. By construction, a the neurons receive the same input at a given moment. The idea of this earning process is to present an exampe to the network and, by using a metric, to determine the neuron in the network most simiar to the presented exampe (center of excitation, winner neuron). Next, we have to modify its weights and those of the center s neighbors. This type of earning is unsupervised, because the neurons move towards the centers of the groups of exampes that they try to represent. The described process was proposed by Kohonen, in his Agorithm of training of Sef Organizing networks [8].

5 VELÁSQUEZ et a.: A NEW SIMILARITY MEASURE TO UNDERSTAND VISITOR BEHAVIOR IN A WEB SITE 5 5. Operation of the Sef-Organizing Feature Map The notion of neighborhood among the neurons provides diverse topoogies. In this case the thoroida topoogy was used [6], [7], which means that the neurons cosest to the ones of the superior edge, are ocated in the inferior and atera edges (see figure 3) Neighborhood of neurons in a thoroida Kohonen net- Fig. 3 work The SOFM s operation needs vectors with the same cardinaity. It is a configurabe parameter in the creation of the visitor behavior vector. Let H be the number of eements in each vector. If a visitor session does not have at east H eements, it is not considered in our anaysis. On the other hand, we ony consider up to the H th component of a session for the visitor behavior vector. Since the visitor behavior vectors have two components for the visited pages (page identifier and time spent on each page), it is necessary to modify both when the neura network changes the weights for the winner neuron and its neighbors. Let N be a neuron in the network and E the visitor behavior vector exampe presented to the network. The vector s time component is modified with a numerica adjustment, i.e., t N,i+ = t N,i f t with i =,..., H. The page component needs another updating scheme. Using the page distance, the difference between page content components is shown in equation 9. D NE = [dp(p h N,, p h E,),..., dp(p h N,H, p h E,H)] (9) Equation 9 represents a vector with distance between pages, i.e., its components are numeric vaues. Then the adjustment is over the D NE expression, i.e., we have D NE = D NE f ρ, with f ρ adjustment factor. Using D NE, it wi be necessary to find a set of pages whose distances with N are cose to D NE. Thus the fina adjustment for page component of the winner neuron and its neighbor neurons is given by equation 0. p N,i+ = π Π/D NE,i dp(π, p h N,i) (0) with Π = {π,..., π Q } the set of a pages in the web site, D NE,i the ith component of D NE. Then given D NE,i we have to find the page π in Π whose dp(π, ph N,i ) is cosest to D NE,i. 6. Practica appication In order to prove the effectiveness of the toos deveoped in this work, a web site was seected, considering the foowing characteristics [3]: It incudes many pages with different information. Each visitor has interest in some pages, but is not interested in others. The web site is maintained by a web master observing pages of interest, i.e., if a page is not visited it wi be removed from the site. The web site seected, is about the first Chiean virtua bank, i.e., it does not have physica branches and a the transactions are made using eectronic means, ike e-mais, portas, etc. We have the foowing information about the web site: Written in Spanish. 27 static web pages. Approximatey eight miion web og registers from the period January to March Sessionization process This task was impemented by a per code and considers 30 minutes as the ongest user session. In order to cean very short sessions, it is necessary to appy a previous heuristic to the data. Ony 6% of the visitors visit 0 or more pages and 8% ess than 4. The average of visited pages is 6. Based on this information, we fix 6 as maximum number of components in a visitor behavior vector, i.e. the parameter H = 6. Vector with more than 6 components are considered ony up to the sixth component. Vector with ess than 4 components are not considered in our anaysis. Finay, appying the above described fiters, approximatey 400,000 visitor behavior vectors were identified. 6.2 Web page content processing Appying web page text fiters, we found 70 different words for our anaysis in the compete web site (i.e. R= 70). Regarding word weights, especiay the specia words (see equation 2), we appied the foowing procedure.

6 6 The web site offers visitors the option to send e- mais to the ca center patform. Then the text sent is a source to identified the most interesting words. In this case, ony 20 specia words were used for page vector cacuation. Using the above described data and appying equation 3, a 3-dimensiona matrix with the simiarity between pairs of pages is created and shown graphicay in figure 4. Page distance web pages i web pages j Fig. 4 Simiarity measure among web pages 200 Since the matrix is symmetric, figure 4 shows ony its superior side. 6.3 Appying Sef-Organizing Feature Maps The SOFM used has 6 input neurons and 32*32 output neurons in the feature map. The thoroida topoogy maintains the continuity in custers [7], which aows to study the transition among the preferences of the visitor from one custer to another. We update a matrix that contains the number of times each neuron wins during the training of the SOFM. Using this information, the custers found by the SOFM are shown in figure 5. The x and y axes are the neuron s position and the z axis is the neuron s winner frequency, with the scae adapted. From figure 5, we can identify four main custers. They are checked using the information contained in the matrix. This aows to find the centroid neurons (winner neuron) of each of the custers. 6.4 Resuts Tabe shows the custers found by the SOFM. The second and third coumn contain the custer s center, represented by the visited pages and the time spent in each one of them. The pages in the web site were abeed with a number to faciitate this anaysis. Tabe 2 shows the main content of each page. IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 Neurons winner frecuency Neurons axis i Fig Neurons axis j Custers among visitor behavior vectors Tabe Visitor behavior custers Custer Pages Visited Time spent in seconds (,3,8,9,47,90) (40,67,75,3,84,43) 2 (00,0,26,28,30,58) (20,69,40,63,07,0) 3 (70,86,50,86,37,97) (4,6,35,5,65,97) 4 (57,69,80,0,05,) (5,80,2,08,30,5) Pages Tabe 2 Pages and their content Content Home page 2,..., 65 Products and Services 66,..., 98 Agreements with other institutions 99,..., 5 Remote services 6,..., 30 Credit cards 3,..., 55 Promotions 56,..., 84 Investments 85,..., 27 Different kinds of credits A detaied custer anaysis together with the banks experts and an interpretation of the web pages visited reveaed the foowing resuts: Custer. The visitors are interested in genera products and services offered by the bank. Custer 2. The visitors search information about credit cards. Custer 3. Visitors are interested in agreements between the bank and other institutions. Custer 4. Visitors are interested in investments and remote services offered by the bank Based on our anaysis we have proposed changes of the structure of the web site, privieging the described information. This new information about the visitor behavior, can be used e.g. for navigation suggestions. For instance, we can cassify a person visiting the web site based on his/her navigation behavior. Based on the custer he or she beongs to we can suggest onine other probaby interesting pages. This way, the web site effectiveness is improved.

7 VELÁSQUEZ et a.: A NEW SIMILARITY MEASURE TO UNDERSTAND VISITOR BEHAVIOR IN A WEB SITE Using an aternative custering agorithm In order to anayze the segmentation found we appied another unsupervised custer agorithms, the weknown k-means [6]. Its main idea is to assign each vector to a set of given custer centroids and then update this centroids given the previousy estabished assignment. This procedure is repeated iterativey unti a certain stopping criterion is fufied. The number of custers to be found (k) is a required input vaue for k-means. Since we found four custers with the SOFM we used k = 4 as input vaue for k- means. We took a random seection from the origina set of training vectors as initia centroids. Given c j,..., cj k as custer centroids in iteration j, we compute c j+,..., c j+ k as custer centroids in iteration j +, according to the foowing steps:. Custer assignment. For each vector in the training set, we determine the custer to which it beongs, using the simiarity introduced in this work. 2. Custer centroid update. Let V j = {v,..., v q j } be the set of q j vectors associated to centroid cj, with =,..., k. We determine the c j+, as a mean of V j,i.e., v i V j / max{ q j j= sm(v i, v j )}, i j 3. Stop when c j+ c j. We appy the k-means over the same set of visitor behavior vectors used in the training of the SOFM. The agorithm converged to the resut shown in tabe Concusions We introduced a methodoogy to understand the visitor behavior in a web site, using a new simiarity measure based on three characteristics derived from the visitor sessions: the sequence of visited pages, their content and the time spent in each one of them. Using this simiarity in a sef organizing map, we can identify custers of visitor sessions, which aow us to study the user behavior in the respective web site. The experiments made with data from a particuar web site showed that the methodoogy used aows to identify meaningfu custers of user sessions, and - using this information - to understand the visitor behavior in the web. The proposed simiarity measure assumes a reativey static web site, i.e., the set of web pages and the text of each page does not change very much in the considered time horizon. If, however, page variation is high, for instance in a newspaper web site, the proposed simiarity can be negativey affected. A soution is to abe automaticay the sites pages in order to compare pages with simiar content. The simiarity introduced can be very usefu to increase the knowedge about the visitor behavior in a web site. As future work it is proposed to improve the presented methodoogy introducing advanced variabes derived from visitor sessions as we as to deveop aternative custering agorithms using the proposed simiarity measure. It wi aso be necessary to continue appying our methodoogy to other web sites in order to get new hints on future deveopments. References Tabe 3 k-means visitor behavior custers Custer Pages Visited Time spent in seconds (2,29,45,2,20,54) (20,69,35,26,34,90) 2 (200,35,0,50,32,28) (3,0,08,30,20,3) 3 (,00,4,28,4,48) (4,76,35,8,89,07) 4 (3,35,56,82,8,7) (25,62,34,03,54,43) Whie custer centroids in tabe 3 are different from those in tabe a more detaied anaysis of the assigned visitor behavior vectors showed simiarities between the custers found. It cannot be concuded, which method gives a better soution; both resuts, however, have been confirmed and interpreted by the business s expert user. The performance of k-means depends directy on the simiarity measure used. In the presented case, it is compex and expensive in cacuation, compared with the traditiona Eucidean distance typicay used in k- means. Comparing computation time of k-means and SOFM showed that k-means requires approximatey 2.5 times more resources than the neura network. [] R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieva, chapter 2. Addison-Wesey 999 [2] M. W. Berry, S. T. Dumais and G.W. O Brien, Using inear agebra for inteigent information retriva, SIAM Review, Vo. 37, pages , December 995 [3] B. Berendt and M. Spiiopouou, Anaysis of navigation behavior in web sites integrating mutipe information systems, The VLDB Journa, Vo. 9, pages 56-75, 200 [4] B. Berent, A. Hotho and G. Stumme, Towards Semantic Web Mining, Proceedings of the First Internationa Semantic Web Conference, pages , Sardinia, Itay, June 9-2, [5] R. Cooey, B. Mobasher, J. Srivastava. Data preparation for mining word wide web browsing patterns. Journa of Knowedge and Information Systems Vo., pages 5-32, 999. [6] J.A. Hartigan and M.A. Wong, A K-means custering agorithm, Journa of the Appied Statistics, Vo. 28, pages 0008, 979 [7] A. Joshi and R. Krishnapuram, On Mining Web Access Logs. In Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowedge Discovery, pages 63-69, [8] T. Kohonen, Sef-Organization and Associative Memory, Springer-Verag, 2nd edition,987.

8 8 IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 [9] V.I. Levenshtein, Binary codes capabe of correcting deetions, insertions and reversas, Sov. Phys. Dok., pages , 966 [0] B. Mobasher, T. Luo, Y. Sung, and J. Zhu, Integrating Web Usage and Content Mining for More Effective Personaization, In Proceedings of the Internationa Conference on E- Commerce and Web Technoogies, September, Greenwich, UK, 2000 [] J. Shavik and G. G. Towe, Knowedge-based artificia neura networks, Artificia Inteigence, vo. 70, no. /2, pages 9-65,994 [2] S. K. Pa, V. Tawar and P. Mitra, Web Mining in Soft Computing Framework: Reevance, State of the Art and Future Directions, IEEE Transactions on Neura Networks, Vo. 3, No. 5 pages 63-77, September, 2002 [3] M. Perkowitz, Adaptive Web Site: Custer Mining and Conceptua Custering for Index Page Synthesis, Dissertation for degree of Doctor of Phiosophy, University of Washington, 200 [4] T.A. Runker and J.C. Bezdek, Web mining with Reationa Custering, Internationa Journa of Approximate Reasoning, Vo. 32, no. 2-3, pages , February, [5] J. Veásquez, H. Yasuda, T. Aoki and Richard Weber, Acquiring Knowedge About Users s Preferences in a Web Site, Proc. First IEEE Int. Conf. on Information Technoogy: Research and Education, pages , Newark, New Jersey, USA,August 2003 [6] J. Veásquez, H. Yasuda, T. Aoki and Richard Weber, Using Sef Organizing Feature Maps to acquire knowedge about visitor behavior in a web site, In Proc. of the Knowedge-Based Inteigent Information & Engineering Systems, pages , Oxford, UK, September, 2003 [7] J. Veásquez, H. Yasuda, T. Aoki and R. Weber, Voice Codification using Sef Organizing Maps as Data Mining Too. Proc. of Second Int. Conf. on Hybrid Inteigent Systems, pages , Santiago, Chie, December, 2002 Yasuda, Hiroshi He received the B.E., M.E. and Dr.E. from the University of Tokyo, Japan in 967, 969, and 972 respectivey. Since joining the Eectrica Communication Laboratories of NTT, in 972, he has been invoved in works on Video Coding, Image Processing, Teepresence, B-ISDN Network and Services, Internet and Computer Communication Appications. After served twenty-five years ( ), with ast position of Vice President, Director of NTT Information and Communication Systems Laboratories at Yokosuka, he eft NTT and has joined the University of Tokyo. He is now Director of The Center for Coaborative Research (CCR). He had served as the Chairman of ISO/IEC JTC/SC29 (JPEG/MPEG Standardization) from 99 to 999. He had aso served as the President of DAVIC (Digita Audio Video Counci) from September 996 to September 998. He received 987 Takayanagi Award, 995 the Achievement Award of EICEJ, The EMMY from The Nationa Academy of Teevision Arts and Science and aso 2000 Chares Proteus Steinmetz Award from IEEE. He is Feow of IEEE, EICEJ, and IPSJ, a member of Teevision Institute. Aoki, Terumasa He is ecturer with the Research Center for Advanced Science and Technoogy, the University of Tokyo. He received his B.S., M.E. and Ph.D. in Information and Communication from the University of Tokyo, Japan in 993, 995, and 998 respectivey. His current research interests are in the fieds of Terabit IP router, access contro of Gigabit LAN/WAN, next-generation video conferencing system, high efficient image coding and management of digita content copyrights. He has received various academic exceent awards such as the 200 IPSJ Yamashita award, the FEEICP Inose award for 994, and the other 4 awards. Veáquez, Juan D. He is doctora student of the RCAST, University of Tokyo. He received the B.E. in Eectrica Engineering and B.E. in Computer Science in 995, the P.E. in Eectrica Engineering and P.E. in Computer Engineering in 996, Master in Computer Science and Master in Industria Engineering in 200 and 2002 respectivey from the University of Chie, Chie. From 997, he is adjunct professor at Computer Science and Industria Engineering Departments, University of Chie. In 998, he received the Orace Data Base Administrator Certification. In the professiona area, he has been consutant in severa ministries of the Repubic of Chie and software companies in Latin America. His research interests incude Data Mining, Web Mining and Very Large Data Bases. Weber, Richard He is assistant professor at the Department of Industria Engineering of the University of Chie and academic director of the Master and Ph.D. program of Operations Management. He received a B.S. in Mathematics, a M.S. and a Ph.D. in Operations Research from Aachen Institute of Technoogy, Germany. His research interests incude Data Mining, Dynamic Data Mining, and Web Mining. He is member of IEEE, ACM and the editoria board of the internationa journa IDA-Inteigent Data Anaysis and has been visiting professor at The Center for Coaborative Research (CCR) at the University of Tokyo.

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