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Association rule mining with mostly associated sequential patterns
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AI
DOI：10.1016/j.eswa.2014.10.049.png)
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摘要
En 中文
In this paper, we address the problem of mining structured data to find potentially useful patterns by association rule mining. Different than the traditional find-all-then-prune approach, a heuristic method is proposed to extract mostly associated patterns (MASPs). This approach utilizes a maximally-association constraint to generate patterns without searching the entire lattice of item combinations. This approach does not require a pruning process. The proposed approach requires less computational resources in terms of time and memory requirements while generating a long sequence of patterns that have the highest co-occurrence. Furthermore, k-item patterns can be obtained thanks to the sub-lattice property of the MASPs. In addition, the algorithm produces a tree of the detected patterns; this tree can assist decision makers for visual analysis of data. The outcome of the algorithm implemented is illustrated using traffic accident data. The proposed approach has a potential to be utilized in big data analytics. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Association rule mining
Interesting rules
Pattern recognition
Big data
Knowledge discovery
Data mining
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期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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