Affiliations: [a] Research Center, College of Computer Science and Information Technology (COCSIT), S.R.T.M University Nanded, Latur, Maharashtra, India | [b] Department of Computer Science, Rajarshi Shahu Mahavidyalya (Autonomous), Latur, India
Correspondence:
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Corresponding author: Drishti Gangaramani, Research Center, College of Computer Science and Information Technology (COCSIT), S.R.T.M University Nanded, Latur, Maharashtra, India. E-mail: [email protected].
Abstract: Associative rule mining is a technique for discovering common patterns and correlations in data sets from different databases, including relational, transactional and other types of data repositories, such as relational databases. Different types of patterns exist in data mining such as frequent patterns, extended patterns, regular patterns etc. Many searches have focused on finding the frequent patterns and very little work has been carried out on negative or rare patterns. It has also been observed that only those items which are positively correlated(frequent) are been executed by various algorithms but very less attention is been given to negatively correlated items. Negatively correlated items also called infrequent items are the items which negate with each other. The items which do not satisfy the minimum threshold value generally are always been ignored by many researchers. Mining of Negative association helps in business such as for customer segmentation, in risk management as well as in medical field. So the main aim of writing this paper is to provide a short overview of various research issues involved in finding out positive and negative associations.
Keywords: Association rule mining, negative association mining, item sets, positive association mining, frequent pattern
