Abstract
Most algorithms for frequent pattern mining use a support-based pruning strategy to prune a combinatorial search space. However, they are not effective for finding correlated patterns with similar levels of support. In additional, traditional patterns mining algorithms rarely consider weighted pattern mining. In this paper, we present a new algorithm, WHFPMiner (Weighted Highly-correlated Frequent Patterns Miner) in which a new objective measure, called weighted h-confidence, is developed to mine weighted highly-correlated frequent patterns with similar levels of weighted support. Adopting an improved weighted FP-tree structure, this algorithm exploits both cross-weighted support and anti-monotone properties of the weighted h-confidence measure for the efficient discovery of weighted hyperclique patterns. A comprehensive performance study shows that WHFPMiner is efficient and fast for finding weighted highly-correlated frequent patterns. Moreover, it generates fewer but more valuable patterns with the high correlation.
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Geng, R., Dong, X., Zhao, J., Xu, W. (2008). WHFPMiner: Efficient Mining of Weighted Highly-Correlated Frequent Patterns Based on Weighted FP-Tree Approach. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_83
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DOI: https://doi.org/10.1007/978-3-540-87734-9_83
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