DSM-TKP: Mining Top-K Path Traversal Patterns over Web Click-Streams

Authors:
Hua-Fu Li;Suh-Yin Lee;Man-Kwan Shan
Affiliations:
National Chiao-Tung University;National Chiao-Tung University;National Chengchi University
Venue:
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2005

Citing 0
Cited 2

A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences

Expert Systems with Applications: An International Journal
Mining top-k maximal reference sequences from streaming web click-sequences with a damped sliding window

Expert Systems with Applications: An International Journal

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Abstract

Online, single-pass mining Web click streams poses some interesting computational issues, such as unbounded length of streaming data, possibly very fast arrival rate, and just one scan over previously arrived click-sequences. In this paper, we propose a new, single-pass algorithm, called DSM-TKP (Data Stream Mining for Top-K Path traversal patterns), for mining top-k path traversal patterns, where k is the desired number of path traversal patterns to be mined. An effective summary data structure called TKP-forest (Top-K Path forest) is used to maintain the essential information about the top-k path traversal patterns of the click-stream so far. Experimental studies show that DSM-TKP algorithm uses stable memory usage and makes only one pass over the streaming data.