• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1345-1352
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• 2014

Data mining is the representative methodology to obtain meaningful information in the era of big data.By Wikipedia, association rule learning is a popular and well researched method for discovering interesting relationship between itemsets in large databases using association thresholds. It is intended to identify strong rules discovered in databases using different interestingness measures. Unlike general association rule, inverse association rule mining finds the rules that a special item does not occur if an item does not occur. If two types of association rule can be simultaneously considered, we can obtain the marketing information for some related products as well as the information of specific product marketing. In this paper, we propose a balanced attributable relative accuracy applicable to these association rule techniques, and then check the three conditions of interestingness measures by Piatetsky-Shapiro (1991). The comparative studies with rule accuracy, relative accuracy, attributable relative accuracy, and balanced attributable relative accuracy are shown by numerical example. The results show that balanced attributable relative accuracy is better than any other accuracy measures.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.49-56
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• 2017

Among the techniques for analyzing big data, the association rule mining is a technique for searching for relationship between some items using various relevance evaluation criteria. This associative rule scheme is based on the direction of rule creation, and there are positive, negative, and inverse association rules. The purpose of this paper is to investigate the applicability of various types of relatively causal strength measures to the types of association rules from the point of view of interestingness measure. We also clarify the relationship between various types of confidence measures. As a result, if the rate of occurrence of the posterior item is more than 0.5, the first measure ($RCS_{IJ1}$) proposed by Good (1961) is more preferable to the first measure ($RCS_{LR1}$) proposed by Lewis (1986) because the variation of the value is larger than that of $RCS_{LR1}$, and if the ratio is less than 0.5, $RCS_{LR1}$ is more preferable to $RCS_{IJ1}$.

• 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.25 no.2
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• pp.365-371
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• 2014

Association rule of the well-studied techniques in data mining is the exploratory data analysis for understanding the relevance among the items in a huge database. This method has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, similarity measures, etc. By typical association rule technique, we generate association rule that satisfy minimum support and confidence values. Support and confidence are the most frequently used, but they have the drawback that they can not determine the direction of the association because they have always positive values. In this paper, we compared support, basic confidence, and three kinds of confidence measures useful for classification model building to overcome this problem. The result confirmed that the causal confirmed confidence was the best confidence in view of the association mining because it showed more precisely the direction of association.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.263-270
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• 2010

Data mining is the process of sorting through large amounts of data and picking out useful information. An important goal of data mining is to discover, define and determine the relationship between several variables. Association rule mining is an important research topic in data mining. An association rule technique finds the relation among each items in massive volume database. Association rule technique consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper explores some problems for two interestingness measures, confidence and net confidence, and then propose a decision process for right association rule generation using these interestingness measures.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.645-661
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• 1997

연속형 변수들의 상관관계와 범주형 변수들의 연관성 측도들을 비교 연구하였다. 이 연구를 위하여 연속형 변수들이며 +1에서 -1까지 완벽한 상관관계를 갖고 있는 2 변량 정규분포를 이용하여 2$\times$2 분할표와 확장하여 일반적인 I$\times$J 분할표를 대신하는 3$\times$3 분할표를 생성하였다. 2 차원 분할표에서 정의된 연관성 측도들을 구하여 논의하였는데 2$\times$2 분할표에서는 교차적비 $\alpha$ 통계량과 교차적비의 함수로 표현되는 Yule [1912]의 Q와 Y의 통계량 그리고 상관계수 R 통계량과 R 통계량의 함수인 P 통계량을 설명하고 생성된 분할표에서 구한 통계량값을 분석하였으며, 3$\times$3 분할표에서는 Pearson의 독립성 검정통계량 $X^2$의 함수로 표현되는 P. T. V 통계량과 Goodman과 Kruskal [1954]의 $\lambda_{C/R}$통계량과 Light와 Margolin [1971]의 $\tau_{R/C}$ 통계량을 설명하고 그 값들을 Pearson의 상관계수와 비교 분석하였다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.179-188
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• 2011

Association rule represents the relationship between items in a massive database by quantifying their relationship, and is used most frequently in data mining techniques. In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively pure association rules by negatively pure support, negatively pure confidence, and negatively pure lift to overcome the problems faced by negative association rule technique. In checking the usefulness of this technique through numerical examples, we could find the direction of association by the sign of the negatively pure association rule measure.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.97-106
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• 2014

The development of big data technology was to more accurately predict diversified contemporary society and to more efficiently operate it, and to enable impossible technique in the past. This technology can be utilized in various fields such as the social science, economics, politics, cultural sector, and science technology at the national level. It is a prerequisite to find valuable information by data mining techniques in order to analyze big data. Data mining techniques associated with big data involve text mining, opinion mining, cluster analysis, association rule mining, and so on. The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, similarity measures, etc.This paper proposed cosine net confidence as association thresholds, and checked the conditions of interestingness measure proposed by Piatetsky-Shapiro, and examined various characteristics. The comparative studies with basic confidence and cosine similarity, and cosine net confidence were shown by numerical example. The results showed that cosine net confidence are better than basic confidence and cosine similarity because of the relevant direction.

• 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.24 no.3
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• pp.523-532
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• 2013

Generally, data mining is the process of analyzing big data from different perspectives and summarizing it into useful information. The most widely used data mining technique is to generate association rules, and it finds the relevance between two items in a huge database. This technique has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, etc. Among many interestingness measures, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The attributably pure confidence and compared confidence are able to determine the direction of the association, but their ranges are not [-1, +1]. So we can not interpret the degree of association operationally by their values. This paper propose a compared and attributably pure confidence to compensate for this drawback, and then describe some properties for a proposed measure. The comparative studies with confidence, compared confidence, attributably pure confidence, and a proposed measure are shown by numerical example. The results show that the a compared and attributably pure confidence is better than any other confidences.