Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis

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

doi:10.1007/978-3-540-30116-5_34

Cited by 70 publications

(60 citation statements)

References 16 publications

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“…We have taken 39 objective rule evaluation indexes to select mined rules [10], visualizing and sorting them depended on users' interest. Although these two procedures are passive support from a viewpoint of the system, we have also identified active system reaction with prediction of user evaluation based on objective rule evaluation indexes and human evaluations.…”

Section: A Procedures To Mine Time-series Rulesmentioning

confidence: 99%

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

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Ohsaki

Yokoi

et al. 2006

New Frontiers in Artificial Intelligence

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-In this paper, we present the implementation of an integrated time-series data mining environment. Time-series data mining is one of key issues to get useful knowledge from data bases. To execute time-series data mining smoothly, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with active human-system interaction. After implementing this environment, we have done a case study to mine time-series rules from blood/urine biochemical test data base on chronic hepatitis patients. Then a physician has evaluated and refined his hypothesis on this environment. We discuss the availability of how much support to mine interesting knowledge for an expert.

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Section: A Procedures To Mine Time-series Rulesmentioning

confidence: 99%

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Abe

Ohsaki

Yokoi

et al. 2006

New Frontiers in Artificial Intelligence

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“…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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“…Second, they are strongly domain-dependent and user-dependent. To avoid these drawbacks, the literature has proposed more than 40 data-driven rule interestingness measures [5], [7], [3]. These measures estimate the degree of interestingness of a rule to the user in a userindependent, domain-independent fashion, and so are much more generic.…”

Section: Introductionmentioning

confidence: 99%

“…The vast majority of works on data-driven rule interestingness measures ignore this question because they do not even show the rules to the user. A notable exception is the interesting work of [5], which investigates the effectiveness of approximately 40 data-driven rule interestingness measures, by comparing their values with the subjective values of the user's interest -what they called real human interest. Measuring real human interest involves showing the rules to the user and ask her/him to assign a subjective interestingness score to each rule.…”

Section: Introductionmentioning

confidence: 99%

“…We investigate the effectiveness of 11 data-driven rule interestingness measures, by comparing them with the user's subjective real human interest. Although we investigate a smaller number of rule interestingness measures, this paper extends the work of [5] by presenting results for eight data sets, whereas [5] did experiments with just one medical data set, a limitation from the point of view of generality of the results.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest

Carvalho

Freitas

Ebecken

2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. In the last few years, the data mining community has proposed a number of objective rule interestingness measures to select the most interesting rules, out of a large set of discovered rules. However, it should be recalled that objective measures are just an estimate of the true degree of interestingness of a rule to the user, the so-called real human interest. The latter is inherently subjective. Hence, it is not clear how effective, in practice, objective measures are. More precisely, the central question investigated in this paper is: "how effective objective rule interestingness measures are, in the sense of being a good estimate of the true, subjective degree of interestingness of a rule to the user?" This question is investigated by extensive experiments with 11 objective rule interestingness measures across eight real-world data sets.

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Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis

Cited by 70 publications

References 16 publications

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

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

Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest

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