• 정보처리학회논문지D
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• 제8D권4호
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• pp.339-346
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• 2001

대화형 환경에서 연관 규칙 탐사 문제는 동일한 데이터베이스에서 다른 최소 지지도로 반복적으로 연관 규칙을 탐사하는 것이다. 이 문제는 반복적으로 연관 규칙을 탐사한다는 사실만 기존의 연관 규칙 탐사와 다를 뿐 기존의 연관 규칙 탐사에서 발생하는 모든 문제를 포함한다. 본 논문은 전 단계에 계산된 후보 항목집합에 대한 정보를 이용함으로써 성능 향상을 가져오는 효율적인 알고리즘을 제안한다. 제안된 알고리즘은 대화형 환경에서 기존의 알고리즘과 수행 시간 측면에서 비교되었다. 성능 비교의 결과로부터 제안하는 알고리즘이 기존의 방법보다 약 10~30% 정도의 상대적 성능 향상 효과가 있음을 알 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.223-226
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• 2009

Recently, Mining negative association rules has received some attention and proved to be useful. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Several algorithms have been proposed. However, there are some questions with those algorithms, for example, misleading rules will occur when the positive and negative rules are mined simultaneously. The chi-squared test that based on the mature theory and Correlation Coefficient can avoid the problem. In this paper, We proposed the algorithm PNCCR based on chi-squared test and correlation is proposed. The experiment results show that the misleading rules are pruned. It suggests that the algorithm is correct and efficient.

• 한국정보시스템학회지:정보시스템연구
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• 제14권3호
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• pp.37-47
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• 2005

Mining of association rules is to find associations among data items that appear together in some transactions or business activities. As of today, algorithms for association rule mining, as well as for other data mining tasks, are mostly applied to relational databases. As XML being adopted as the universal format for data storage and exchange, mining associations from XML data becomes an area of attention for researchers and developers. The challenge is that the semi-structured data format in XML is not directly suitable for traditional data mining algorithms and tools. In this paper we present an encoding method to encode XML tree-nodes. This method is used to store the XML data in Value Table and Transaction Table that can be easily accessed via indexing. The hierarchical relationship in the original XML tree structure is embedded in the encoding. We applied this method to association rules mining of XML data that may have missing data.

• Journal of Information Processing Systems
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• 제4권1호
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• pp.27-32
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• 2008

Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.

• 산업경영시스템학회지
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• 제23권54호
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• pp.27-36
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• 2000

The problem of discovering association rules is an emerging research area, whose goal is to extract significant patterns or interesting rules from large databases and several algorithms for mining association rules have been applied to item-oriented sales transaction databases. Data warehouses and OLAP engines are expected to be widely available. OLAP and data mining are complementary; both are important parts of exploiting data. Our study shows that data cube is an efficient structure for mining association rules. OLAP databases are expected to be a major platform for data mining in the future. In this paper, we present an efficient and effective algorithm for mining association rules using data cube. The algorithm can be applicable to enhance the power of competitiveness of business organizations by providing rapid decision support and efficient database marketing through customer segmentation.

• 산업경영시스템학회지
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• 제23권54호
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• pp.1-11
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• 2000

This study suggests the partition algorithm for updating the discovered association rules in large database, because a database may allow frequent or occasional updates, and such update may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. the Partition algorithm updates strong association rules efficiently in the whole update database reuseing the information of the old large itemsets. Partition algorithms that is suggested in this study scans an incremental database in view of the fact that it is difficult to find the new set of large itemset in the whole updated database after an incremental database is added to the original database. This method of generating large itemsets is different from that of FUP(Fast Update) and KDP(Kim Dong Pil)

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.59-62
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• 2001

This paper discusses a data mining technique for time series analysis of traffic data, which provides useful knowledge for network configuration management. Commonly, a network designer must employ a combination of heuristic algorithms and analysis in an interactive manner until satisfactory solutions are obtained. The problem of heuristic algorithms is that it is difficult to deal with large networks and simplification or assumptions have to be made to make them solvable. Various data mining techniques are studied to gain valuable knowledge in large and complex telecommunication networks. In this paper, we propose a traffic pattern association technique among network nodes, which produces association rules of traffic fluctuation patterns among network nodes. Discovered rules can be utilized for improving network topologies and dynamic routing performance.

• 산업경영시스템학회지
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• 제22권50호
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• pp.183-193
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• 1999

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.

• 대한산업공학회지
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• 제32권3호
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• pp.242-252
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• 2006

Data mining is the exploration and analysis of huge amounts of data to discover meaningful patterns. One of the most important data mining problems is association rule mining. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm is proposed that is based on a link structure between transactions. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.