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.

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Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification

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

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Association rule mining is the process of finding some relations among the attributes/attribute values of huge database based on support value. Most existing association mining techniques are developed to generate frequent rules based on frequent itemsets generated on market basket datasets. A common property of these techniques is that they extract frequent itemsets and prune the infrequent itemsets. However, such infrequent or rare itemsets and consequently the rare rules may provide valuable information. So, many applications demand to mine such rare association rules which have low support but higher confidence. This paper presents a method to generate both frequent and rare itemsets and consequently the rules. The effectiveness of the rules has been validated over several real life datasets.

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