Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Mining sequential patterns from temporal streaming data

Abstract. In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time and to the best of our knowledge, no method has been proposed for mining sequential patterns in data streams. We argue that the main reason is the combinatory phenomenon related to sequential pattern mining. In this paper, we propose an algorithm based on sequences alignment for mining approximate sequential patterns in Web usage data streams. To meet the constraint of one scan, a greedy clustering algorithm associated to an alignment method are proposed. We will show that our proposal is able to extract relevant sequences with very low thresholds

Position Coded Pre-Order Linked WAP-Tree for Web Log Sequential Pattern Mining

Abstract. Web access pattern tree algorithm mines web log access sequences by first storing the original web access sequence database on a prefix tree (WAP-tree). WAP-tree algorithm then mines frequent sequences from the WAP-tree by recursively re-constructing intermediate WAP-trees, starting with their suffix subsequences. This paper proposes an efficient approach for using the preorder linked WAP-trees with binary position codes assigned to each node, to mine frequent sequences, which eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. Experiments show huge performance advantages for sequential mining using prefix linked WAP-tree technique