Generalizing Itemset Mining in a Constraint Programming Setting

Besson, J.; Boulicaut, Jean-François; Guns, Tias; Nijssen, Siegfried

doi:10.1007/978-1-4419-7738-0_5

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…They deal with noise through a post-processing step; our proposed approach has a direct way to describe fault-tolerant itemsets in the framework of CP. Directly mining fault-tolerant patterns has been studied by Besson et al, also in a constraint programming setting [1], [2]. In our work, the fault-tolerant frequent itemset formalism is inspired by the one proposed by Liu et al [11], which also imposes relative error constraints on rows and columns.…”

Section: Related Workmentioning

confidence: 99%

Mining Local Staircase Patterns in Noisy Data

Van

Fierroy²,

Guns

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising.

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Section: Related Workmentioning

confidence: 99%

Mining Local Staircase Patterns in Noisy Data

Van

Fierroy²,

Guns

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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“…This notion of association rules is very general, and much research has been invested into constraint-association rule mining, which can efficiently limit the search to rules that satisfy constraints, such as rules having a negative consequent [ 10 ].…”

Section: Definitionsmentioning

confidence: 99%

A primer to frequent itemset mining for bioinformatics

Naulaerts¹,

Meysman²,

Bittremieux³

et al. 2013

Briefings in Bioinformatics

108

107

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Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

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