Abstract
Understanding Web users’ behaviour is an important criterion for improving the overall experience of Web users. Web Pattern Mining is one such field that helps us to mine useful behavioural patterns and draw conclusions from them after careful analysis. Efficient Web pattern mining is a challenge taking into consideration the enormous quantities of raw Web log data and explosive growth of information in the Web. In this chapter, we propose a novel algorithm called Bidirectional Growth based mining Cyclic Behaviour Analysis of Web sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to effectively prefetch Web pages, thus reducing the users’ perceived latency. In other words, BGCAP grow patterns bidirectionally along both ends of detected patterns and allows faster pattern growth with fewer levels of recursion thus eliminating unnecessary candidates and support for efficient pruning of invalid candidates. Due to these facts, BGCAP requires only (log n+1) levels of recursion for mining n Web Sequential Patterns. Our experimental results show that the Web Sequential Patterns and in turn prefetching rules generated using BGCAP is 5–10% faster for different data sizes and generates about 5–15% more prefetching rules than TD-Mine.
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Venugopal, K.R., Srikantaiah, K.C. (2020). Mining and Cyclic Behaviour Analysis of Web Sequential Patterns. In: Web Recommendations Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2513-1_3
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DOI: https://doi.org/10.1007/978-981-15-2513-1_3
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