Abstract
HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules.
Highlights
Association rule mining is one of the important research contents in the field of data mining [1], which is widely used in finance, internet, medicine, and other fields
Like Apriori algorithm, HotSpot algorithm has one obvious shortcoming: support selection needs to be set artificially based on experience and cannot be set accurately according to the actual scale of the problem [3]
If the support threshold is set too low, a cumbersome and complicated tree structure may be generated; if the support threshold is set too high, some associated intervals existing in the rare target attribute values may be ignored. erefore, in the process of support selection, multiple comparison experiments are needed to determine the optimal support based on the mining results
Summary
Association rule mining is one of the important research contents in the field of data mining [1], which is widely used in finance, internet, medicine, and other fields. General association rule algorithms can only process discrete data, such as classical Apriori algorithm [2]. E HotSpot algorithm selected in this paper can directly mine association rules and dynamically acquire the range of real number intervals without discretization of real data, avoiding the influence of subjective factors in discretization, and the processing speed is extremely fast. Like Apriori algorithm, HotSpot algorithm has one obvious shortcoming: support selection needs to be set artificially based on experience and cannot be set accurately according to the actual scale of the problem [3]. Wang presented an improved K-means algorithm by combining the agglomerative hierarchical clustering algorithm to select the initial cluster centers for HotSpot discovery in Internet public opinions [6]. The S_HotSpot algorithm generates an association rule tree of which every node stores a support of corresponding frequent itemsets
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