Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining

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

doi:10.1007/978-3-540-24677-0_110

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…In this section, we first present the results of an empirical evaluations with the dataset obtained from the result of a meningitis data mining [1], that of two times of hepatitis data mining [2], [3]. Based on the experimental results, we discuss the followings: accuracy of rule evaluation models, minimum training subsets of the learning algorithms, and contents of the learned rule evaluation models.…”

Section: A Constructing a Rule Evaluation Modelmentioning

confidence: 98%

Evaluating Learning Costs to Predict Human Interests with Rule Evaluation Models based on Objective Indices

Abe

Tsumoto

Yokoi

et al. 2007

2007 IEEE/ICME International Conference on Complex Medical Engineering

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In this paper, we present an evaluation of learning costs of rule evaluation models based on objective indices for an iterative rule evaluation support method in data mining postprocessing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To estimate learning costs for predicting human interests with objective rule evaluation indices, we have done the two case studies with actual data mining results, which include different phases of human interests. With regard to these results, we discuss about learning costs to predict real human interests with objective rule evaluation indices.

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Section: A Constructing a Rule Evaluation Modelmentioning

confidence: 98%

Evaluating Learning Costs to Predict Human Interests with Rule Evaluation Models based on Objective Indices

Abe

Tsumoto

Yokoi

et al. 2007

2007 IEEE/ICME International Conference on Complex Medical Engineering

Self Cite

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“…To date, many techniques have been published such as Piatetsky-Shapiro's rule-interest function [25], Smyth and Goodman's JMeasure [29], Agrawal and Srikant's itemset measures [1], and Gray and Orlowska's interestingness [10]. There have also been many application studies of the techniques in many fields such as marketing [1] and medical domains [21,24]. For example, Piatetsky-Shapiro introduced his ruleinterest function which was used to quantify the correlation between two attributes arbitrarily chosen, and the obtained knowledge is expressed as classification rules [25].…”

Section: Related Workmentioning

confidence: 99%

An information value based approach to design procedure capture

Ishino¹,

Jin²

2006

Advanced Engineering Informatics

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“…Interestingness measures have to support Kdd process through system-human interaction [71], [1]. Many works (for instance [6], [53], [36], [37], [87], [85], [51], [17], [60], [89], [86], [70]) have formally extracted and studied several specificities of various measures, and the importance of objective evaluation criteria of interestingness measures has already been focused on by [75] and [26].…”

Section: Introductionmentioning

confidence: 99%

Association Rule Interestingness Measures: Experimental and Theoretical Studies

Lenca¹,

Vaillant²,

Meyer

et al. 2007

Studies in Computational Intelligence

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Summary.It is a common problem that Kdd processes may generate a large number of patterns depending on the algorithm used, and its parameters. It is hence impossible for an expert to assess these patterns. This is the case with the wellknown Apriori algorithm. One of the methods used to cope with such an amount of output depends on using association rule interestingness measures. Stating that selecting interesting rules also means using an adapted measure, we present a formal and an experimental study of 20 measures. The experimental studies carried out on 10 data sets lead to an experimental classification of the measures. This study is compared to an analysis of the formal and meaningful properties of the measures. Finally, the properties are used in a multi-criteria decision analysis in order to select amongst the available measures the one or those that best take into account the user's needs. These approaches seem to be complementary and could be useful in solving the problem of a user's choice of measure.

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Comparison between Objective Interestingness Measures and Real Human Interest in Medical Data Mining

Cited by 8 publications

References 18 publications

Evaluating Learning Costs to Predict Human Interests with Rule Evaluation Models based on Objective Indices

Evaluating Learning Costs to Predict Human Interests with Rule Evaluation Models based on Objective Indices

An information value based approach to design procedure capture

Association Rule Interestingness Measures: Experimental and Theoretical Studies

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