Exploration of Soft Computing Approaches in Itemset Mining

Exploration of Soft Computing Approaches in Itemset Mining

Jyothi Pillai (Bhilai Institute of Technology, India) and O. P. Vyas (Indian Institute of Information Technology, India)
DOI: 10.4018/978-1-4666-9562-7.ch091
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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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2. Data Mining Definition Of Terms

Data mining is the technique of automatic finding of hidden valuable patterns, relationships and information elicitation from huge volume of data stored in data bases, data warehouses and other data repositories, in order to help make better business decisions, finding sales trends, in developing smarter marketing campaigns and in predicting customer loyalty. Two categories of Data mining tasks are; Descriptive Mining and Predictive Mining. The Descriptive Mining techniques include Clustering, Association Rule Discovery and Sequential Pattern Discovery, are used to find human-interpretable patterns that describe the data in the form of clusters, itemsets, association rules and sequential patterns. The Predictive Mining techniques such as Classification, Regression, Deviation Detection, are used to classify objects or to predict future values of other variables. The most frequently used Data Mining techniques are Classification, Prediction, Clustering and ARM.

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