Investigation of Rule Interestingness in Medical Data Mining

Ohsaki, Miho; Kitaguchi, Saori; Yokoi, Hideto; Yamaguchi, Takahira

doi:10.1007/11423270_10

Cited by 13 publications

(15 citation statements)

References 20 publications

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“…The results of our and other researchers' surveys [1][2][3]5] show that interestingness measures can be categorized with the several factors in Table 1. The subject to evaluate rules, a computer or human user, is the most important categorization factor.…”

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 53%

“…In addition, they may offer a human user unexpected new viewpoints. Although the validity of objective measures has been theoretically proven and/or experimentally discussed using some benchmark data [1][2][3], very few attempts have been made to investigate their comparative performance and the relation between them and real human interest for a real application [5]. Our investigation will be novel in this light.…”

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 98%

“…Our investigation will be novel in this light. Coverage [5] P P (A), P (A): Probability of antecedent. Prevalence [5] P P (C), P (C): Probability of consequent.…”

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 99%

“…Coverage [5] P P (A), P (A): Probability of antecedent. Prevalence [5] P P (C), P (C): Probability of consequent. Precision [3,5] P P (C|A), P (C|A): Conditional probability of consequent for antecedent.…”

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis

Ohsaki

Kitaguchi

Okamoto

et al. 2004

Lecture Notes in Computer Science

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Abstract. This research empirically investigates the performance of conventional rule interestingness measures and discusses their practicality for supporting KDD through human-system interaction in medical domain. We compared the evaluation results by a medical expert and those by selected measures for the rules discovered from a dataset on hepatitis. Recall, Jaccard, Kappa, CST, χ 2 -M, and Peculiarity demonstrated the highest performance, and many measures showed a complementary trend under our experimental conditions. These results indicate that some measures can predict really interesting rules at a certain level and that their combinational use will be useful.

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Section: Conventional Rule Interestingness Measuresmentioning

confidence: 53%

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 98%

“…Our investigation will be novel in this light. Coverage [5] P P (A), P (A): Probability of antecedent. Prevalence [5] P P (C), P (C): Probability of consequent.…”

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 99%

Section: Conventional Rule Interestingness Measuresmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis

Ohsaki

Kitaguchi

Okamoto

et al. 2004

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…But, in the literature, they seek to find a correlation between real human interest and objective interestingness measures [5,[37][38][39]. BM_IRIL also proposes a new feature weighting technique that takes benefit maximization issues into account.…”

Section: Introductionmentioning

confidence: 99%

Modeling interestingness of streaming association rules as a benefit-maximizing classification problem

Aydın

Güvenir

2009

Knowledge-Based Systems

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a b s t r a c tIn a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the last month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrive, the association rule learning algorithm is executed on the last set of transactions, resulting in new association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as ''association rule set stream" or ''streaming association rules" and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to another and also over time for a given expert. This paper proposes a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors related to the interestingness of the association rules, and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real market dataset. The results show that the model can successfully select the interesting ones.

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