• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.117-129
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• 2023

High-utility itemset mining (HIUM) has emerged as a key data-mining paradigm for object-of-interest identification and recommendation systems that serve as frequent itemset identification tools, product or service recommendation systems, etc. Recently, it has gained widespread attention owing to its increasing role in business intelligence, top-N recommendation, and other enterprise solutions. Despite the increasing significance and the inability to provide swift and more accurate predictions, most at-hand solutions, including frequent itemset mining, HUIM, and high average- and fast high-utility itemset mining, are limited to coping with real-time enterprise demands. Moreover, complex computations and high memory exhaustion limit their scalability as enterprise solutions. To address these limitations, this study proposes a model to extract high-utility frequent closed itemsets based on an improved cumulative summary list structure (CSLFC-HUIM) to reduce an optimal set of candidate items in the search space. Moreover, it employs the lift score as the minimum threshold, called the cumulative utility threshold, to prune the search space optimal set of itemsets in a nested-list structure that improves computational time, costs, and memory exhaustion. Simulations over different datasets revealed that the proposed CSLFC-HUIM model outperforms other existing methods, such as closed- and frequent closed-HUIM variants, in terms of execution time and memory consumption, making it suitable for different mined items and allied intelligence of business goals.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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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.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.392-394
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• 2003

연관 규칙 마이닝은 일반적으로 않은 빈발항목집합과 연관 규칙을 생성하며, 생성된 연관 규칙은 상호 포함관계에 있거나 중복되는 경우가 많다. 이는 효과적인 마이닝 뿐 아니라 마이닝의 활용 효용성을 떨어뜨린다. 이를 해결하기 위하여 연관 규칙 마이닝과 동일한 성능을 가지며 생성되는 규칙의 수를 줄일 수 있는 빈발 폐쇄 항목집합 마이닝이 제안되었다. 본 연구에서는 연관규칙 마이닝 방법 중 가장 우수한 성능을 가지는 ARCS 알고리즘을 개선한 빈발 폐쇄 항목집단 마이닝을 제안한다.