Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research e orts. The term Web mining has been used intwo distinct ways. The rst, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web usage mining, is the process of mining for user browsing and access patterns. In this paper we de ne Web mining and present an overview of the various research issues, techniques, and development e orts. We brie y describe WEBMINER, a system for Web usage mining, and conclude this paper by listing research issues. 1

Most web sites are set up with little knowledge on the navigational behaviour of the users accessing them. Feedback on the occurring navigation patterns can significantly aid site owners in efficiently (re)organizing the hyperspace they present to their visitors. In this study, we present the Web Utilization Miner WUM, a mining system for the discovery of interesting navigation patterns. The interestingness criteria for navigation patterns are dynamically specified by the human expert using WUM's mining language MINT. MINT supports the specification of criteria of statistical, structural and textual nature. To discover the navigation patterns satisfying the expert's criteria, WUM exploits an innovative aggregated storage representation for the information in the web server log.

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.

Web Usage Mining is the application of data mining techniques to Web clickstream data in order to extract usage patterns. As Web sites continue to grow in size and complexity, the results of Web Usage Mining have become critical for a number of applications such as Web site design, business and marketing decision support, personalization, usability studies, and network trac analysis. The two major challenges involved in Web Usage Mining are preprocessing the raw data to provide an accurate picture of how a site is being used, and ltering the results of the various data mining algorithms in order to present only the rules and patterns that are potentially interesting. This thesis develops and tests an architecture and algorithms for performing Web Usage Mining. An evidence combination framework referred to as the information lter is developed to compare and combine usage, content, and structure information about a Web site. The information lter automatically identi es the discovered ...

Web site design is currently based on thorough investigations about the interests of web site visitors and on less investigated assumptions about their exact behaviour. Concrete knowledge on the way visitors navigate in a web site could prevent disorientation and help owners in placing important information exactly where the visitors look for it. Our Web Utilization Miner tool can provide such knowledge. The general problem we address is: Given a number of traversed paths, discover subpaths with structural or statistical properties of interest. In fact, we anticipate that not all nodes in a subpath are of equal importance. Hence, we allow that subpaths having only some nodes in common be combined into a pattern that shows the desired properties as a whole. To capture the ambiguous expressions of this problem, we provide a powerful mining language, by which the expert can specify the desired structural and statistical properties of the patterns to be constructed. To efficient...

We introduce intelligent web caching algorithms that employ predictive models of web requests; the general idea is to extend the LRU policy of web and proxy servers by making it sensible to web access models extracted from web log data using data mining techniques. Two approaches have been studied in particular, frequent patterns and decision trees. The experimental results of the new algorithms show substantial improvement over existing LRU-based caching techniques, in terms of hit rate. We designed and developed a prototypical system, which supports data warehousing of web log data, extraction of data mining models and simulation of the web caching algorithms.

The world-wide web (WWW) is the largest distributed information space and has grown to encompass diverse information resources. Although the web is growing exponentially, the individual’s capacity to read and digest content is essentially fixed. The full economic potential of the web will not be realized unless enabling technologies are provided to facilitate access to web resources. Currently web personalization is the most promising approach to remedy this problem, and web mining, particularly web-usage mining, is considered a crucial component of any efficacious web-personalization system. In this paper, we describe a complete framework for web-usage mining to satisfy the challenging requirements of web-personalization applications. For on-line and anonymous web personalization to be effective, web usage mining must be accomplished in real time as accurately as possible. On the other hand, web-usage mining should allow a compromise between scalability and accuracy to be applicable to real-life websites with numerous visitors. Within our web-usage-mining framework, we introduce a distributed user-tracking approach for accurate, scalable, and implicit collection of the usage data. We also propose a new model, the feature-matrices (FM) model, to discover and interpret users’ access patterns. With FM, various spatial

The web is a large source of information that can be turned into knowledge. Part of this knowledge concerns the usage of the web itself and is invaluable to the organization of web sites that meet their purposes and prevent disorientation. Data mining is already being used to throw light in various aspects of web utilization. For one major aspect, the discovery of navigation patterns, we show that a new mining model is necessary. We formalize the notion of navigation pattern, introduce a model for navigation pattern discovery by extending the classical model of association rules' discovery, and establish the processing framework of this model. Conventional tools for association rule discovery and for sequence analysis cannot deal with this discovery problem. However, we show how they can be used as preprocessors to reduce the search space before the actual mining phase. 1 Introduction As the information offered in the Web grows daily, obtaining information from it becomes more and mor...

The paper presents a vertical application of data warehousing and data mining technology: intelligent web caching. We introduce several ways to construct intelligent web caching algorithms that employ predictive models of web requests; the general idea is to extend the LRU policy of web and proxy servers by making it sensible to web access models extracted from web log data using data mining techniques. Two approaches have been studied in particular, one based on association rules and another based on decision trees.

Abstract. The success of personal information agents depends on their capacity to both identify relevant information for users and proactively recommend context-relevant information. In this paper, we propose an approach to enable proactive context-aware recommendation based on the knowledge of both user interests and browsing patterns. The proposed approach analyzes the browsing behavior of users to derive a semantically enhanced context that points out the information which is likely to be relevant for a user according to its current activities. 1