• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.367-376
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• 2015

Today, big data has become a hot keyword in that big data may be defined as collection of data sets so huge and complex that it becomes difficult to process by traditional methods. Clustering method is to identify the information in a big database by assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. The similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we computed upper and lower limits for probability interestingness measure based similarity measures without marginal probability such as Yule I and II, Michael, Digby, Baulieu, and Dispersion measure. And we compared these measures by real data and simulated experiment. By Warrens (2008), Coefficients with the same quantities in the numerator and denominator, that are bounded, and are close to each other in the ordering, are likely to be more similar. Thus, results on bounds provide means of classifying various measures. Also, knowing which coefficients are similar provides insight into the stability of a given algorithm.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.857-864
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• 2015

By Wikipedia, data mining is the computational process of discovering patterns in huge data sets involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. Clustering or cluster analysis is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. The similarity measures being used in the clustering may be classified into various types depending on the characteristics of data. In this paper, we computed bounds for similarity measures based on the probabilistic interestingness measure with partially marginal probability such as Peirce I, Peirce II, Cole I, Cole II, Loevinger, Park I, and Park II measure. We confirmed the absolute value of Loevinger measure wasthe upper limit of the absolute value of any other existing measures. Ordering of other measures is determined by the size of concurrence proportion, non-simultaneous occurrence proportion, and mismatch proportion.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1127-1135
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• 2012

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. We generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.117-124
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• 2013

Association rule of data mining techniques is the method to quantify the relationship between a set of items in a huge database, andhas been applied in various fields like internet shopping mall, healthcare, insurance, and education. There are three primary interestingness measures for association rule, support and confidence and lift. Confidence is the most important measure of these measures, and we generate some association rules using confidence. But it is an asymmetric measure and has only positive value. So we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure (PIM) with all marginal proportions (AMP) to solve this problem. The comparative studies with support, confidences, lift, chi-square statistics, and some similarity measures by PIM with AMPare shown by numerical example. As the result, we knew that the similarity measures by PIM with AMP could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values, and select the best similarity measure by PIM with AMP.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1189-1197
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• 2013

Data mining is the process of analyzing a huge database from different perspectives and summarizing it into useful information. One of the well-studied problems in data mining is association rule generation. Association rule mining finds the relationship among several items in massive volume database using the interestingness measures such as support, confidence, lift, etc. Typical applications for this technique include retail market basket analysis, item recommendation systems, cross-selling, customer relationship management, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. This paper propose causal association thresholds to compensate for this problem, and then check the three conditions of interestingness measures. The comparative studies with basic and causal association thresholds are shown by numerical example. The results show that causal association thresholds are better than basic association thresholds.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.891-899
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• 2010

Association rule is the technique to represent the relationship between two or more items by numerical representing for the relevance of each item in vast amounts of databases, and is most being used in data mining. The basic thresholds for association rule are support, confidence, and lift. these are used to generate the association rules. We need standardization of lift because the range of lift value is different from that of support and confidence. And also we need standardization of support and confidence to compare objectively association level of antecedent variables for one descendant variable. In this paper we propose a method for standardization of association thresholds considering marginal probability for each item to grasp objectively and exactly association level, check the conditions for association criteria and then compare association thresholds with standardized association thresholds using some concrete examples.