• 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.

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

• 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.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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.611-618
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• 2015

By Wikipedia, big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. 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. Association rule is a well researched method for discovering interesting relationships between itemsets in huge databases and has been applied in various fields. There are positive, negative, and inverse association rules according to the direction of association. If you want to set the evaluation criteria of association rule, it may be desirable to consider three types of association rules at the same time. To this end, we proposed a balanced comparative confidence considering sensitivity, specificity, false positive, and false negative, checked the conditions for association threshold by Piatetsky-Shapiro, and compared it with comparative confidence and inversely comparative confidence through a few experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.207-214
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• 2021

This study aims to analyze the effect of the level of accounts receivable on firm profitability. It is possible to find the optimal level of accounts receivable that maximizes profitability. In this study, 6,632 samples were selected from manufacturing companies listed on the Korean Stock Exchange from 2001 to 2018. We used the fixed effect panel model to analyze the model equation. There is a positive (+) relationship between the profitability of a company, the Return on Assets (ROA), and accounts receivable (AR). Also, this relationship has a nonlinear relationship or a reverse-U shape. There is an optimal level of accounts receivables, which results in profitability increase up to a certain extent, but subsequently, profitability decreases when accounts receivables exceed this level. In the case of monopoly companies with a higher-than-average market share, the coefficient between accounts receivable and firm profitability is greater than that for competitors with a lower market share than average. It supports the hypothesis that Titman (1984) suggested, that trade credit is important for enhancing corporate profitability. It is confirmed that accounts receivables play an important role in enhancing firm profitability and it is necessary to understand this well from the corporate standpoint.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.281-294
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• 2004

Association measures are applied to various applications, including information retrieval and data mining. Each association measure is subject to a close examination to its tendency to prefer high or low frequency level because it has a significant impact on the performance of applications. This paper examines the frequency level preference(FLP) tendency of some popular association measures using artificially generated cooccurrence data, and evaluates the results. After that, a method of how to adjust the FLP tendency of major association measures such as cosine coefficient is proposed. This method is tested on the cooccurrence-based query expansion in information retrieval and the result can be regarded as promising the usefulness of the method. Based on these results of analysis and experiment, implications for related disciplines are identified.

• 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.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.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.1-8
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• 2007

The purpose of this study is to explore relationship of computer curriculums between department of computer education in universities and specialized vocational high schools. We sampled 15 computer curriculums from department of computer education, 17 computer curriculums from specialized high schools and 41 curriculums from vocational high schools. And we compare and analyze the number of subjects and times on all of curriculums. As a result, curriculums in department of computer education is lacking in subjects for domains of multimedia, computer graphics and practical computer graphics. It is low on the relationship of computer curriculums between department of computer educations and IT-related high schools. Therefore, this study concludes by proposing computer curriculums of department of computer education which should reflect the subjects.