Choosing the Right Lens: Finding What is Interesting in Data Mining

Luo, Geng; Hamilton, Howard J.

doi:10.1007/978-3-540-44918-8_1

Cited by 40 publications

(36 citation statements)

References 32 publications

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“…Nevertheless, it is now well-known that the support-confidence framework is rather poor to evaluate rule interestingness (Brin et al, 1997a;Bayardo & Agrawal, 1999;Tan et al, 2004). Numerous rule interestingness measures have been proposed to complement this framework (lists can be found for example in (Tan et al, 2004;Geng & Hamilton, 2007)). To unify all the approaches, we propose below a definition of the concept of rule interestingness measure.…”

Section: Interestingness Measuresmentioning

confidence: 99%

Semantics-Based Classification of Rule Interestingness Measures

Blanchard¹,

Guillet²,

Kuntz³

2009

Post-Mining of Association Rules

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Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index.

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Section: Interestingness Measuresmentioning

confidence: 99%

Semantics-Based Classification of Rule Interestingness Measures

Blanchard¹,

Guillet²,

Kuntz³

2009

Post-Mining of Association Rules

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“…Geng, and Hamilton reviewed the large number of measures briefly, compared their properties and specified their roles in data mining [2]. Support and confidence are two measures that were usually used in early algorithms.…”

Section: Introductionmentioning

confidence: 99%

Proposing an efficient combination of interesting measures for mining association rules via NSGA-II

Rokh

Mirvaziri

Eftekhari

2014

2014 International Congress on Technology, Communication and Knowledge (ICTCK)

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Selecting accurate and simple association rules that efficiently cover all data samples is very important in knowledge discovery. There are several measures to assess accuracy and relations in a rule. This poses a challenge for researchers to select effective measures. Combining different measures via multi-objective evolutionary algorithms is an effective method to select suitable association rules. Therefore in this paper NSGA-II algorithm is employed for rule selection via different combination of existing measures (support, certainty factor, change of support, Yao and Liu's one way support, cosine and lift) as objectives. The contributions of the paper are twofold. Firstly, some existing measures are modified. Secondly, several experiments are done to evaluate the performance of different combinations of measures through NSGA-II. The experimental results show that the combination of certainty factor and square of cosine measures are more effective in rule selection.

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“…In this paper, by examining data/text mining algorithms existed nowadays [1], [2], [3], [5], [6], [9], [12], [20], we adopted data slicing algorithm [6]. By executing this algorithm, we calculated the acupoint sets in the treatment of periarthritis of shoulder in two dimensions.…”

Section: Introductionmentioning

confidence: 99%

Searching Rules of Acupuncture on Periarthritis of Shoulder by Data Slicing Algorithm

Guo

Zheng

et al. 2011

2011 International Conference on Internet Technology and Applications

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Acupuncture is an important component in traditional Chinese medicine. It is efficient in dealing with some diseases, esp. periarthritis of shoulder. In this paper, focused on periarthritis of shoulder, we mined the data set of literature with data slicing algorithm. Acupoints are calculated on two dimensions. The one-dimension calculates the frequencies of acupuncture points existed in literature. The two-dimension calculates the frequencies of acupoint pairs co-existed in literature. By examining the results, we found the sets of acupuncture points in dealing with this disease. By checking these results, not only regular acupoints in the textbook are mined out, but also some other acupoints which are widely used in the clinical practices are also found. What's more, these acupoints might be taken as complementarities for the textbook.

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Choosing the Right Lens: Finding What is Interesting in Data Mining

Cited by 40 publications

References 32 publications

Semantics-Based Classification of Rule Interestingness Measures

Semantics-Based Classification of Rule Interestingness Measures

Proposing an efficient combination of interesting measures for mining association rules via NSGA-II

Searching Rules of Acupuncture on Periarthritis of Shoulder by Data Slicing Algorithm

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