Association mining

By Aaron Ceglar, John F. Roddick. CSUR, 2006

371 citations recorded by the source corpus.

The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/cafd933228e0dd97ef913055e2f0009defbd5faa

How researchers see this paper

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In Artificial Intelligence

1. Jun-Feng Qu
2. Mengchi Liu

In Computational Theory and Mathematics

1. Jun-Feng Qu
2. Mengchi Liu
3. Philippe Fournier-Viger

In Information Systems

1. Jun-Feng Qu
2. Mengchi Liu
3. Philippe Fournier-Viger

Citations in the corpus

• Mining high utility itemsets without candidate generation (2012, background): “Among the topics, the high utility itemset mining problem is one of the most important, and it derives from the famous frequent itemset mining problem [7, 8].”