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Exploration of Soft Computing Approaches in Itemset Mining
Abstract
Data Mining is largely known to extract knowledge from large databases in an attempt to discover existing trends and newer patterns. While data mining refers to information extraction, soft computing is more inclined to information processing. Using Soft Computing, the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for achieving tractability, robustness, and low-cost solutions can be revealed. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Soft computing techniques are Fuzzy Logic (FL), Neural Network (NN), Genetic Algorithm (GA), Rough Set (RS), etc. For handling different types of uncertainty in huge data, FL and RS are highly suitable. NNs are a nonparametric, robust technique and provide good learning and generalization capabilities in data-rich environments. GAs provide efficient search algorithms for selecting a model, from mixed-media data, based on some priority criterion. In one of its realms, Association Rule Mining (ARM) and Itemset mining have been a focus of research in data mining for a decade, including finding most frequent item sets and corresponding association rules and extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. The objective of this chapter is to explore the usage of Soft Computing approaches in itemset utility mining, both frequent and rare itemsets. In addition, a literature review of applications of soft computing techniques in temporal mining is described.
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