Association rule mining problems can be considered as a multi-objective problem rather than as a single objective one. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions present in the rule. This objective gives the accuracy of the rules extracted from the database. Comprehensibility is measured by the number of attributes involved in the rule and tries to quantify the understandability of the rule. Interestingness measures how much interesting the rule is.Using these three measures as the objectives of rule mining problem, this article uses a Pareto based genetic algorithm to extract some useful and interesting rules from any market-basket type database. Based on experimentation, the algorithm has been found suitable for large databases.
Extracting frequent patterns from databases has always been an imperative task for the data mining community. Literature has endowed plentiful endeavors to this research area with significant breakthroughs. Mining of rare patterns although being subsided has proved to be of vital importance in many domains. The application of rare pattern mining is inevitable and thus has become an emerging field of research. However, discovering rare patterns from databases comes up with numerous challenges. This article provides a concise overview of the various research issues involved in rare pattern mining through experimental analysis using real-life and synthetic datasets. Rare pattern mining being a new area has abundant scope and there are still certain gaps that need to be filled. In this article, we also present some viable future directions for the researchers to better perceive the area of rare pattern mining.
KeywordsRare patterns • Frequent patterns • Rare item dilemma • Challenges B Anindita Borah
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.