| Summary: | 碩士 === 國立臺灣師範大學 === 資訊教育學系 === 94 === Abstract
Mining Recent Path Traversal Patterns on Webclick Streams
by
Chun-Wei Hsieh
Frequent traversal patterns extracted from the history data represent the mining results of long term but not necessary the recent trend. However, the web administrators are usually interesting in the traversal path of recent users. Therefore, an algorithm, called RPTP, for mining recent path traversal patterns on webclick streams is proposed in this thesis. In our approach, the lossy counting techniques are applied to maintain frequent and semi-frequent patterns in a sliding window of recent user sessions. Hence, frequent patterns on webclick streams are discovered efficiently in a dynamic way. It is not necessary for RPTP to store the original data. Instead, the appearing information of recent frequent and semi-frequent patterns is recorded. Moreover, the strategies for mining closed frequent patterns from the constructed data structures are provided to avoid generating redundant information in the mining result. Accordingly, the concept of closed patterns is applied to reduce the number of maintained patterns. The experimental results show that the RPTP achieves an efficient execution time under a reasonable memory requirement. Furthermore, by comparing with the related work, RPTP provides a shorter response time to reflect the change of frequent traversal patterns on webclick streams.
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