Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree

Abstract

Abstract. Sequential mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. An important application of sequential mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences. This paper proposes a more efficient approach for using the WAP-tree to mine frequent sequences, which totally eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. The proposed algorithm builds the frequent header node links of the original WAP-tree in a pre-order fashion and uses the position code of each node to identify the ancestor/descendant relationships between nodes of the tree. It then, finds each frequent sequential pattern, through progressive prefix sequence search, starting with its first prefix subsequence event. Experiments show huge performance gain over the WAP-tree technique.

SEQUEST: mining frequent subsequences using DMA-Strips

“…Similarly [11] formulated the problem of mining frequent subsequences from genetic sequences where each element of the sequences consists of an atomic item. Recently, Ezeife and Lu [4] developed an efficient technique PLWAP based on the concept of WAPTree that mines frequent subsequences of items. PLWAP is reported to have desirable scalability properties.…”

“…Data mining strives to find frequent patterns in data [1][2][3][4]12]. A task in data mining is characterized by the type of data, structures and patterns to be found [8,9].…”

“…sequences whose elements consist of multi items, is a more difficult problem and poses more challenges. In this paper, we will limit our scope of study to the first problem by comparing our technique, SEQUEST, with the PLWAP algorithm [4]. Nonetheless, SEQUEST using DMA-Strips is designed to tackle both sub problems of mining frequent subsequences.…”

“…The structure guided method is adapted from the TMG approach used for enumerating subtrees in our previous work [8]. Experiments utilizing very large databases of sequences which compare our technique with the existing technique, PLWAP [4], demonstrate the effectiveness of our proposed technique. …”

Mining Sequential Access Pattern with Low Support From Large Pre-Processed Web Logs

“…A sequential pattern mining algorithm mines the sequence database looking for repeating patterns (known as frequent sequences) that can be used later by end users or management to find associations between the different items or events in their data for purposes such as marketing campaigns, business reorganization, prediction and planning. With increase in the use of the world wide web for E-Commerce businesses, web services and others, web usage mining has appeared in the literature as one of the most prevalent application areas of sequential pattern mining (Han et al, 2000;Ezeife and Lu, 2005;El-Sayed et al, 2004;Han et al, 2004;Madan and Madan, 2010) This study focuses on sequential pattern mining techniques including those applicable to web usage mining.…”