Mining association rules with multiple minimum supports

By B. Liu, Wynne Hsu, Yiming Ma. 1999

Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in reallife applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules. Experiment results show that the technique is very effective.

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Citations in the corpus

Foundations and Advances in Data Mining (2005, background): “This paper is mainly based on [32], [33],[35], and [36].”
Mining topics in documents: standing on the shoulders of big data (2014, methodology): “It then mines must-links M from S using a multiple minimum supports frequent itemset mining algorithm [20].”
Identifying comparative sentences in text documents (2006, background): “Note that multiple minimum support association rule mining was studied in [18].”
Association Rules Mining: A Recent Overview (2006, background): “In [20], the authors present an approach to the rare item problem.”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “Association rule mining with class unbalance is often achieved by specifying multiple support levels for each class to account for the difference in class frequency [112].”
Recommender Systems (2016, unknown): “Various enhancements of the method, which use support levels specific to the item at hand are discussed in [358, 359, 365].”
Cost-sensitive boosting for classification of imbalanced data (2007, background): “These classi-ﬁcation systems involve decision trees [1,8–10], support vector machines [9,11–13], neural networks [9], Bayesian network [6], nearest neighbor [8,14] and the newly reported associative classiﬁcation approaches [15,16].”
Effective personalization based on association rule discovery from web usage data (2001, methodology): “[8] proposed a mining method with multiple minimum support”
Mining with rarity: a unifying framework (2004, background): “This problem has been called the rare item problem [37].”
A review of associative classification mining (2007, methodology): “These multiple support AC techniques use either Apriori or FP-growth methods to find frequent ruleitems and are quite similar to an earlier developed association rule discovery algorithm called MSapriori (Liu et al., 1999).”
Alternative Interest Measures for Mining Associations in Databases (2003, background): “In [16], the authors present an approach to the rare item problem.”