• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.259-261
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• 2002

데이터마이닝 분야에서 빈발항목집합 탐사에 관한 연구는 활발히 진행되어 왔지만 여전히 많은 메모리 공간과 시간을 필요로 한다. 특히 apriori 알고리즘에 기반한 방법들은 긴 패턴이 생성될수록 지수적으로 시간과 공간이 증가한다. 최근에 발표된 fp-growth는 일반적인 데이터 집합에서 우수한 성능을 보이나 희소 데이터 집합에서 효율적인 성능을 보여주지 못한다. 본 논문에서는 길이가 2인 빈발항목집합 L2에 기반한 L2-tree 구조를 제안한다. 또한 L2-tree에서 빈발항목집합을 탐사하는 L2-traverse 알고리즘을 제안한다. L2-tree는 L2를 기반으로 하기 때문에 L2가 상대적으로 적은 희소 데이터 집합 환경에서 적은 메모리 공간을 사용하게 된다. L2-traverse 알고리즘은 별도의 추출 데이터베이스를 생성하는 FP-growth와 달리 단순히 L2-tree를 오직 한번의 깊이 우선 탐사를 통해 빈발항목집합을 찾는다. 최적화 기법으로써 길이가 3인 빈발항목집합 L3가 되지 않는 L2 패턴들을 미리 제거하는 방법으로 C3-traverse 알고리즘을 제안하며 실험을 통해 기존 알고리즘과 비교 검증한다.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.817-828
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• 2005

The main research problems in a mining frequent itemsets are reducing memory usage and processing time of the mining process, and most of the previous algorithms for finding frequent itemsets are based on an Apriori-property, and they are multi-scan algorithms. Moreover, their processing time are greatly increased as the length of a maximal frequent itemset. To overcome this drawback, another approaches had been actively proposed in previous researches to reduce the processing time. However, they are not efficient on a sparse .data set This paper proposed an efficient mining algorithm for finding frequent itemsets. A novel tree structure, called an $L_2$-tree, was proposed int, and an efficient mining algorithm of frequent itemsets using $L_2$-tree, called an $L_2$-traverse algorithm was also proposed. An $L_2$-tree is constructed from $L_2$, i.e., a set of frequent itemsets of size 2, and an $L_2$-traverse algorithm can find its mining result in a short time by traversing the $L_2$-tree once. To reduce the processing more, this paper also proposed an optimized algorithm $C_3$-traverse, which removes previously an itemset in $L_2$ not to be a frequent itemsets of size 3. Through various experiments, it was verified that the proposed algorithms were efficient in a sparse data set.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.205-213
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• 2010

This study uses the FP-tree algorithm, one of the mining techniques. This study is an attempt to suggest a new recommended system using a modified FP-tree algorithm which yields an association rule based on frequent 2-itemsets extracted from the transaction database. The modified recommended system consists of a pre-processing module, a learning module, a recommendation module and an evaluation module. The study first makes an assessment of the modified recommended system with respect to the precision rate, recall rate, F-measure, success rate, and recommending time. Then, the efficiency of the system is compared against other recommended systems utilizing the sequential pattern mining. When compared with other recommended systems utilizing the sequential pattern mining, the modified recommended system exhibits 5 times more efficiency in learning, and 20% improvement in the recommending capacity. This result proves that the modified system has more validity than recommended systems utilizing the sequential pattern mining.