Algorithms for association rule mining — a general survey and comparison

Hipp, Jochen; Güntzer, Ulrich; Nakhaeizadeh, Gholamreza

doi:10.1145/360402.360421

Cited by 754 publications

(342 citation statements)

References 17 publications

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“…While the second step mentioned above is straightforward (generating association rules from frequent itemsets), the first step is computationally expensive due to the large number of possible frequent itemsets (or patterns of values) [72]. Popular algorithms for efficiently discovering frequent patterns include Apriori [8], Eclat [141], and FP-Growth [67].…”

Section: Correlations and Association Rulesmentioning

confidence: 99%

Profiling relational data: a survey

Abedjan¹,

2015

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Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.

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Section: Correlations and Association Rulesmentioning

confidence: 99%

Profiling relational data: a survey

Abedjan¹,

2015

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“…Consequently, rules are frequently used as a representation for local pattern discovery tasks such as association rule mining (Agrawal et al, 1995;Hipp et al, 2000) and subgroup discovery (Klösgen, 1996;Wrobel, 1997;Scheffer and Wrobel, 2002;Lavrač et al, 2004).…”

Section: From Local To Global Patternsmentioning

confidence: 99%

From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms

Fürnkranz

2005

Lecture Notes in Computer Science

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Abstract. Separate-and-conquer or covering rule learning algorithms may be viewed as a technique for using local pattern discovery for generating a global theory. Local patterns are learned one at a time, and each pattern is evaluated in a local context, with respect to the number of positive and negative examples that it covers. Global context is provided by removing the examples that are covered by previous patterns before learning a new rule. In this paper, we discuss several research issues that arise in this context. We start with a brief discussion of covering algorithms, their problems, and review a few suggestions for resolving them. We then discuss the suitability of a well-known family of evaluation metrics, and analyze how they trade off coverage and precision of a rule. Our conclusion is that in many applications, coverage is only needed for establishing statistical significance, and precision is the metric that should be optimized for rules. The main problem with optimizing precision is its unreliability for low example sizes, which is mainly caused by overfitting. We then report some preliminary experiments that addresses this problem by meta-learning a predictor for the true accuracy of a rule based on its coverage on the training set.

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“…See Hipp, Güntzer, and Nakhaeizadeh (2000) for a recent survey of the problem and Han and Plank (1996) for a somewhat older comparison of some selected algorithms.…”

Section: Association Rulesmentioning

confidence: 99%

ART: A Hybrid Classification Model

et al. 2004

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Abstract. This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

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Algorithms for association rule mining — a general survey and comparison

Cited by 754 publications

References 17 publications

Profiling relational data: a survey

Profiling relational data: a survey

From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms

ART: A Hybrid Classification Model

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