• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2865-2872
• /
• 2018

In the field of data mining technique, there are various methods such as association rules, cluster analysis, decision tree, neural network. Among them, association rules are defined by using various association evaluation criteria such as support, confidence, and lift. Agrawal et al. (1993) first proposed this association rule, and since then research has been conducted by many scholars. Recently, studies related to crossover entropy have been published (Park, 2016b). In this paper, we proposed a purely symmetric J measure considering directionality and purity in the previously published J measure, and examined its usefulness by using examples. As a result, it is found that the pure symmetric J measure changes more clearly than the conventional J measure, the symmetric J measure, and the pure crossover entropy measure as the frequency of coincidence increases. The variation of the pure symmetric J measure was also larger depending on the magnitude of the inconsistency, and the presence or absence of the association was more clearly understood.

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.151-167
• /
• 2003

Although there exist numerous measures of association, most of them are lacking in generality in that they do not intend to measure the association between heterogeneous type of random variables. On the other hand, many statistical analyzes dealing with complex data sets require a very sophisticate measure of association. In this note, the p-value of independence tests is utilized to obtain a measure of association. The proposed measure of association have some consistency in measuring association between various types of random variables.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.523-536
• /
• 2021

Although there exist numerous measures of association, most of them are lacking in generality in that they do not intend to measure the association between heterogeneous type of random variables. On the other hand, many statistical analyzes dealing with complex data sets require a very sophisticate measure of association. In this note, the p-value of independence tests is utilized to obtain a measure of association. The proposed measure of association have some consistency in measuring association between various types of random variables.

• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.1
• /
• pp.41-48
• /
• 2003

In this paper, we present a new measure to evaluate the interestingness of association rules. Ultimately. to evaluate whether a rule is interesting or not is subjective. However, an interestingness measure is useful in that it shows the cause for pruning uninteresting rules statistically or logically. Some interestingness measures have been developed in association rules mining. We present an overview of interestingness measures and propose a new measure. A comparative study of some interestingness measures is made on an example dataset and a real dataset. Our experiments show that the new measure can avoid the discovery of misleading rules.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.2
• /
• pp.353-362
• /
• 2016

By Wikipedia, data mining is the process of discovering patterns in a big data set involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. and database systems. Association rule is a method for discovering interesting relations between items in large transactions by interestingness measures. Association rule interestingness measures play a major role within a knowledge discovery process in databases, and have been developed by many researchers. Among them, the Hellinger measure is a good association threshold considering the information content and the generality of a rule. But it has the drawback that it can not determine the direction of the association. In this paper we proposed a signed Hellinger measure to be able to interpret operationally, and we checked three conditions of association threshold. Furthermore, we investigated some aspects through a few examples. The results showed that the signed Hellinger measure was better than the Hellinger measure because the signed one was able to estimate the right direction of association.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.10a
• /
• pp.197-213
• /
• 2003

One of the well-studied problems in data mining is the search for association rules. The goal of association rule mining is to find all the rules with support and confidence exceeding some user specified thresholds. In this paper we consider the relation between the measure of association and the criteria of association rule for ordinal data.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.6
• /
• pp.1127-1135
• /
• 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 Korea Fashion and Costume Design Association
• /
• v.14 no.4
• /
• pp.139-150
• /
• 2012

This study aims to understand the importance of stage costumes, examine and analyze their theoretical ideas in order to propose new designs and support the studies and advances of stage costumes in play. The writer operated and produced the costume designs of Shakespeare's play, Measure for Measure as a costume director, which was played on September 16 to 17, 2011 at the Haneul Theater in the National Theater of Korea and on September 3, 2011 at the Jinnam Munye Theater. The study was followed by 1. Proposing a new modern point of view of the design of the traditional dresses in 16th to 17th centuries. 2. Expressing a symbolism based on personality, role and nature of characters in the play by a creative and modern image of the dress in color, line, and silhouette, which are basic factors of a clothes design.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.25-38
• /
• 2004

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. In this paper we present the relation between the measure of association based on chi square statistic and the criteria of association rule for nominal database and propose the objective criteria for association.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.1
• /
• pp.37-42
• /
• 2014

The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. The abstract should be written as one paragraph and should not contain tabular material or numbered references. At the end of abstract, keywords should be given in 3 to 5 words or phrases.