Classification of objective interestingness measures
نویسندگان
چکیده
منابع مشابه
Classification of objective interestingness measures
The creation of the interestingness measures for evaluating the quality of the association rule based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures i...
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In this paper, we propose a new approach to evaluate the behavior of objective interestingness measures on association rules. The objective interestingness measures are ranked according to the most significant interestingness interval calculated from an inversely cumulative distribution. The sensitivity values are determined by this interval in observing the rules having the highest interesting...
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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...
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An important problem in the area of data mining is the development of eeective measures of interestingness for ranking discovered knowledge. In this paper, we propose ve principles that any measure must satisfy to be considered useful for ranking the interestingness of summaries generated from databases. We investigate the problem within the context of summarizing a single dataset which can be ...
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One of the most important steps in any knowledge discovery task is the interpretation and evaluation of discovered patterns. To address this problem, various techniques, such as the chi-square test for independence, have been suggested to reduce the number of patterns presented to the user and to focus attention on those that are truly statistically signiicant. However, when mining a large data...
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ژورنال
عنوان ژورنال: EAI Endorsed Transactions on Context-aware Systems and Applications
سال: 2016
ISSN: 2409-0026
DOI: 10.4108/eai.12-9-2016.151678