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Efficient Data Mining for Path Traversal Patterns
March/April 1998 (vol. 10 no. 2)
pp. 209-221

Abstract—In this paper, we explore a new data mining capability that involves mining path traversal patterns in a distributed information-providing environment where documents or objects are linked together to facilitate interactive access. Our solution procedure consists of two steps. First, we derive an algorithm to convert the original sequence of log data into a set of maximal forward references. By doing so, we can filter out the effect of some backward references, which are mainly made for ease of traveling and concentrate on mining meaningful user access sequences. Second, we derive algorithms to determine the frequent traversal patterns—i.e., large reference sequences—from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences; one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed. It is shown that the option of selective scan is very advantageous and can lead to prominent performance improvement. Sensitivity analysis on various parameters is conducted.

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Index Terms:
Data mining, traversal patterns, distributed information system, World Wide Web, performance analysis.
Citation:
Ming-Syan Chen, Jong Soo Park, Philip S. Yu, "Efficient Data Mining for Path Traversal Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 10, no. 2, pp. 209-221, March-April 1998, doi:10.1109/69.683753
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