• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11c
• /
• pp.1765-1768
• /
• 2002

최근 들어 웹사이트 개인화(Web Personalization)에 관한 연구가 활발히 진행되고 있다. 웹 개인화는 클러스터링과 같은 데이터 마이닝 기법을 이용하여 개개의 사용자에게 가장 흥미를 갖을만한 URLs의 집합을 예측하는 것이라 할 수 있다. 기존에는 웹 트랜잭션을 클러스터링 하기 위해서 사용자의 방문여부에 따라 트랜잭션을 비트벡터(bit vector)로 표현하였다. 하지만 이것은 웹 트랜잭션의 클러스터링에 있어서 사용자의 흥미를 배제하고 단순히 방문여부만을 반영하게 된다. 이에 본 논문에서는 사용자의 흥미도(Interestingness)를 반영할 수 있도록 보완된 웹 트랜잭션 모델을 제시하고 제안된 트랜잭션 모델을 적용한 유사도 비교방법을 제안한다. 그리고 성능평가를 통하여 제안한 방법이 기존 방법에 비해 클러스터링의 정확성을 높임을 보인다.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.939-948
• /
• 2012

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between items in a massive database based on the interestingness measures such as support, confidence, lift, etc. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control.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 attributable and pure confidence to overcome the problems faced by negative association rule technique, and then we checked three conditions for interestingness measure. The comparative studies with negative confidence, negatively pure confidence, and negatively attributable and pure confidence are shown by numerical examples. The results show that the negatively attributable and pure confidence is better than negative confidence and negatively pure confidence.

• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.4
• /
• pp.857-868
• /
• 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
• /
• v.28 no.1
• /
• pp.49-56
• /
• 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
• /
• v.24 no.6
• /
• pp.1189-1197
• /
• 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.

• Asia pacific journal of information systems
• /
• v.18 no.2
• /
• pp.95-114
• /
• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.04a
• /
• pp.261-270
• /
• 2000

개념 계층(Concept Hierarchy)은 데이터베이스 분야에서 사용되는 대표적인 배경 지식(Background Knowledge)으로써, 데이터베이스에 내재되어 있는 구조적인 정보, 데이터의 분포, 영역전문가(Domain Expert)에 의해 주어지는 외부 지식 등이 반영되어 있다. 개념 계층의 특성상 부모(parent)-자식(child) 관계가 있는 두 노드가 있을 때, 한 노드의 값으로부터 다른 노드의 값을 추정할 수 있다. 이 추정된 값을 기대치라고 하고, 한 노드의 값으로부터 추정된 기대치와 실제치가 상당히 상이한 값을 보이는 노드가 있을 때, 이를 흥미롭다(interesting)라고 할 수 있다. 그러나 아직까지 개념계층상에서의 흥미로운 부분 탐색에 대한 연구가 없었으며, 흥미로움(interestingness)의 척도(measurement)에 대한 연구로서는 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등이 있다. 그러나 이런 흥미도의 척도에 관한 연구도 연관규칙에 한정되어 이루어졌으므로 개념계층상의 데이터에 적용하기 위해서는 약간의 수정 및 새로운 정의가 필요하다. 본 논문에서는 데이터의 특성에 따른 개념계층이 존재할 때, 이를 이용하여 기대치와 실제치가 상이한 흥미로운 부분을 발견하고자 하며, 이를 위하여 개념계층이 존재할 때, 이를 이용하여 기대치와 실제치가 상이한 흥미로운 부분을 발견하고자 하며, 이를 위하여 개념계층상에서의 흥미도의 척도를 제안하고 흥미로운 부분을 탐색하는 방법을 기술하고자 한다. 또한 데이터마이닝의 결과인 연관규칙을 개념계층에 적용하여 연관규칙을 통해 얻어질 수 있는 기대치를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.

• Journal of Intelligence and Information Systems
• /
• v.6 no.2
• /
• pp.77-89
• /
• 2000

개념 계층(Concept Hierarchy)은 데이터베이스 분야에서 사용되는 대표적인 배경 지식(Background Knowledge)으로써, 데이터베이스에 내재되어 있는 구조적인 정보, 데이터의 분포, 영역전문가 (Domain Expert)에 의해 주어지는 외부 지식 등이 반영되어 있다. 개념계층의 특성상 부모(parent)-자 식(child) 관계가 있는 두 노드가 있을 때, 한 노드의 값으로부터 다른 노드의 값을 추정할 수 있다 이 추정된 값을 기대치라고 하고, 한 노드의 값으로부터 추정된 기대치와 실제치가 상당히 상이한 값을 보이는 노드가 있을 때, 이를 흥미롭다(interesting)고 말할 수 있다. 그러나 아직까지 개념계층 상에서의 흥미로운 부분 탐색에 대한 연구가 없었으며, 흥미로움(interestingness)의 척도(measurement) 에 대한 연구로서는 신뢰도(confidence),리프트(lift),컨빅션(conviction)등이 있었다. 그러나 이런 흥미도 의 척도에 관한 연구도 연관규칙에 한정되어 이루어졌으므로 개념계층상의 데이터에 적용하기 위해 서는 약간의 수정 및 새로운 정의가 필요하다. 본 논문에서는 데이터의 특성에 따른 개념계층이 존재할 때, 이를 이용하여 기대치와 실제치가 상이한 흥미로운 부분을 발견하고자 하며, 이를 위하여 개념계층상에서의 흥미도의 척도를 제안하고 흥미로운 부분을 탐색하는 방법을 기술하고자 한다. 또한 데이터마이닝의 결과인 연관규칙을 개념 계층에 적용하여 연관규칙을 통해 얻어질 수 있는 기대치를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용 가능하리라 여겨진다.

• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.888-895
• /
• 2021

The aim of this study is to investigate the effects of climate messages using vegetable consumption arguments on social media. This study examines how interestingness and comprehension of messages affect social media responses and participation in campaigns as well as the moderating effect of the interest in vegetarianism (IV). The results indicate that interestingness and comprehension led to social media responses and participation in campaigns. The moderating effect of IV was significant.