• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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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)

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.23-31
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• 2001

Data mining can be either directed or undirected. One way of thinking about it is that we use undirected data mining to recognize relationship in the data and directed data mining to explain those relationships once they have been found. Several data mining techniques have received considerable research attention. In this paper, we propose an algorithm for discovering association rules as directed data mining and applying them to classification. In the first phase, we find frequent closed itemsets and association rules. After this phase, we construct the decision trees using discovered association rules. The algorithm can be applicable to customer relationship management.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.28-30
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• 2003

컴퓨팅 환경의 발달로 방대한 양의 정보들이 매우 빠른 속도로 생성되고 있다. 구성 요소가 지속적으로 발생되는 무한 집합으로 정의되는 데이터 스트림에 대한 마이닝 방법은 이들 정보로부터 중요한 지식을 효과적으로 얻을 수 있는 방법으로 최근 들어 다양한 방법들이 활발히 제안되고 있다. 이러한 마이닝 방법에서는 지속적으로 확장되는 데이터 스트림의 특성으로 수행과정에서 메모리 사용량을 가용 범위 내로 제한하는 것이 중요한 고려 사항이 되고 있다. 본 논문에서는 데이터 스트림에서 빈발 항목을 탐색하는데 있어서 가용 메모리 범위에서 최적의 메모리를 사용하여 최상의 마이닝 결과를 얻을 수 있도록 하는 메모리 사용량 최적화 방법을 제시한다.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.1-17
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• 2008

Data mining is defined as the process of discovering meaningful and useful pattern in large volumes of data. In particular, finding associations rules between items in a database of customer transactions has become an important thing. Some data structures and algorithms had been proposed for storing meaningful information compressed from an original database to find frequent itemsets since Apriori algorithm. Though existing method find all association rules, we must have a lot of process to analyze association rules because there are too many rules. In this paper, we propose a new data structure, called a Frequent Pattern Network (FPN), which represents items as vertices and 2-itemsets as edges of the network. In order to utilize FPN, We constitute FPN using item's frequency. And then we use a clustering method to group the vertices on the network into clusters so that the intracluster similarity is maximized and the intercluster similarity is minimized. We generate association rules based on clusters. Our experiments showed accuracy of clustering items on the network using confidence, correlation and edge weight similarity methods. And We generated association rules using clusters and compare traditional and our method. From the results, the confidence similarity had a strong influence than others on the frequent pattern network. And FPN had a flexibility to minimum support value.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.15-22
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• 2008

In this paper, we propose an efficient algorithm using only one-time database scan. The proposed algorithm creates the bipartite graph which indicates relationship of large items and transactions including the large items. And then we can find large itemsets using the bipartite graph. The bipartite graph is generated when database is scanned to find large items. We can't easily find transactions which include large items in the large database. In the bipartite graph, large items and transactions are linked each other. So, we can trace the transactions which include large items through the link information. Therefore the bipartite graph is a indexed database which indicates inclusion relationship of large items and transactions. We can fast find large itemsets because proposed method conducts only one-time database scan and scans indexed the bipartite graph. Also, it don't generate candidate itemsets.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.103-112
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• 2022

The goal of pattern mining is to identify novel patterns in a database. High utility itemset mining (HUIM) is a research direction for pattern mining. This is different from frequent itemset mining (FIM), which additionally considers the quantity and profit of the commodity. Several algorithms have been used to mine high utility itemsets (HUIs). The original BPSO algorithm lacks local search capabilities in the subsequent stage, resulting in insufficient HUIs to be mined. Compared to the transfer function used in the original PSO algorithm, the V-shaped transfer function more sufficiently reflects the probability between the velocity and position change of the particles. Considering the influence of the acceleration factor on the particle motion mode and trajectory, a nonlinear acceleration strategy was used to enhance the search ability of the particles. Experiments show that the number of mined HUIs is 73% higher than that of the original BPSO algorithm, which indicates better performance of the proposed algorithm.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.199-207
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• 2008

The FP-tree(Frequency Pattern Tree) mining association rules algorithm was proposed to improve mining performance by reducing DB scan overhead dramatically, and it is recognized that the performance of it is better than that of any other algorithms based on different approaches. But the FP-tree algorithm needs a few more memory because it has to store all transactions including frequent itemsets of the DB. This paper implements a FP-tree algorithm on a general purpose UNK system and compares it with the DHP(Direct Hashing and Pruning) algorithm which uses hash tree and direct hash table from the point of memory usage and execution time. The results show surprisingly that the FP-tree algorithm is poor than the DHP algorithm in some cases even if the system memory is sufficient for the FP-tree. The characteristics of the test data are as follows. The site of DB is look, the number of total items is $1K{\sim}7K$, avenrage length of transactions is $5{\sim}10$, avergage size of maximal frequent itemsets is $2{\sim}12$(these are typical attributes of data for large-scale convenience stores).

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.765-774
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• 2006

An association rule discovery, a technique to analyze the stored data in databases to discover potential information, has been a popular topic in stream data system. Most of the previous researches are concerned to single stream data. However, this approach may ignore in mining to multidimensional stream data. In this paper, we study the techniques discovering the association rules to multidimensional stream data. And we propose a AR-MS method reflecting the characteristics of stream data since make the summarization information by one data scan and discovering the association rules for significant rare data that appear infrequently in the database but are highly associated with specific event. Also, AR-MS method can discover the maximal frequent item of multidimensional stream data by using the summarization information. Through analysis and experiments, we show that AR-MS method is superior to other previous methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.383-393
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• 2013

Data stream mining is a technique to obtain the useful information by analyzing the data generated in real time. In data stream mining technology, frequent itemset mining is a method to find the frequent itemset while data is transmitting, and these itemsets are used for the purpose of pattern analyze and marketing in various fields. Existing techniques of finding frequent itemset mining are having problems when a malicious attacker sniffing the data, it reveals data provider's real-time information. These problems can be solved by using a method of inserting dummy data. By using this method, a attacker cannot distinguish the original data from the transmitting data. In this paper, we propose a method for privacy preserving frequent itemset mining by using the technique of inserting dummy data. In addition, the proposed method is effective in terms of calculation because it does not require encryption technology or other mathematical operations.