• Journal of KIISE:Databases
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• v.32 no.3
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• pp.219-227
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• 2005

In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scanning. A frequent itemset is subset of an itemset which is accessed by a transaction. For each item, if informations about transactions accessing the item are exist, it is possible to generate frequent itemsets only by the extraction of items haying an identical transaction ID. Proposed method in this paper generates the data structure which stores transaction ID for each item by only one database scanning and generates 2-frequent itemsets by using the hash technique at the same time. k(k$\geq$3)-frequent itemsets are simply found by comparing previously generated data structure and transaction ID. Proposed algorithm can efficiently generate frequent itemsets by only one database scanning .

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.621-630
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• 2003

Web logs are the information recorded by a web server when users access the web sites, and due to a speedy rising of internet usage, the worth of their practical use has become increasingly important. Analyzing such logs can use to determine the patterns representing users' navigational behavior in a Web site and restructure a Web site to create a more effective organizational presence. For these applications, the generally used key methods in many studies are association rules and sequential patterns based by Apriori algorithms, which are widely used to extract correlation among patterns. But Apriori inhere inefficiency in computing cost when applied to large databases. In this paper, we develop a new algorithm for mining interesting patterns which is faster than Apriori algorithm and recommendation agents which could provide a system manager with valuable information that are accessed sequentially by many users.

• Journal of Korea Multimedia Society
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• v.5 no.1
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• pp.120-127
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• 2002

The common problems found in the data mining methods current in use have following problems. First: It is ineffective in searching for frequent items due to changing of minimal support values. Second: It is not adaptable to occurring of unuseful relation rules. Third: It is very difficult to re-use preceding results while adding new transactions. In this paper, we introduce a new method named as SPM-IRG(Selective Patters Mining using item Relation Graph), that is designed to solve above listed problems. SPM-IRG method creates a frequent items using minimal support values obtained by investigating direct or indirect relation of all items in transaction. Moreover, the new method can minimize inefficiency of existing method by constructing frequent items using only the items that we are interested.

• 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).

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.1
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• pp.107-119
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• 2006

Recently with developments of an internet and web techniques, the amount of data that are stored in database is increasing rapidly. So the range of adaption in database has been expanded and a research of Data Mining techniques finding useful skills from the huge database has been progressed. Many original algorithms have been developed by cutting down the item set and the size of database isn't required in the entire course of creating frequent item sets. Although those skills could save time in some course, it requires too much time for adapting those techniques in other courses. In this paper, an algorithm is proposed. In an Transaction Database that the length of it's transactions are short or the number of items are relatively small, this algorithm scans a database once by using a Hashing Technique and at the same time, stores all parts of the set, can be appeared at each transaction, in an Hash-table. So without an influence of n minimum percentage of support, it can discover a set of frequent items in more shorter time than the time what is used by an original algorithm.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.91-98
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• 2004

DHP mining association rules algorithm maintains previously independent direct hash table to reduce the sire of hash tree containing the frequency number of each candidate large itemset. It performs pruning by using the direct hash table when the hash tree is constructed. The mort large the size of direct hash table increases, the higher the effort of pruning becomes. Especially, the effect of pruning in phase 2 which generate 2-large itemsets is so high that it dominates the overall performance of DHP algorithm. So, following the speedy trends of producing VLM(Very Large Memory) systems, extreme increment of direct hash table size is being tried and one of those trials is perfect hash table in phase 2. In case of using perfect hash table in phase 2, we found that some rearrangement of DHP algorithm got about 20% performance improvement compared to simply ｜H$_2$｜ reconfigured DHP algorithm. In this paper, we examine the feasibility of perfect hash table in phase 2 and propose PHP algorithm, a rearranged DHP algorithm, which uses the characteristics of perfect hash table sufficiently, then make an analysis on the results in experimental environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.283-291
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• 2000

인터넷 서비스 제공자들이 관심을 가지고 있는 것 중 하나는 인터넷 사용자들의 서비스 이용 패턴과 경향을 분석하는 것이다. 이를 통해 매출 증대와 실제 경영에 도움이 되는 사용자의 특성을 이해할 수 있기 때문이다. 이와 관련된 기본적인 접근방법은 사용자가 웹 서버에 접근했을 때 서버에 남는 웹 로그를 분석하여 사용자 패턴을 분석하는 것이다. 웹 로그 분석에 전형저인 통계기법이 사용되고 있다. 그러나 단순 통계 기법만으로는 알려지지 않는 데이터들 사이에 숨겨진 유용한 정보를 찾는 데에는 한계가 있다. 최근에는 이러한 한계를 극복하기 위해 데이터 마이닝 기술을 이용한 새로운 접근 방법이 시도되고 있다. 그러나 실제로 웹 로그에서부터 데이터 마이닝 기술을 이용하는 데에는 전처리 과정의 어려움과 실제 유용한 패턴을 어떻게 정의하는 가가 어려운 문제이다. 본 연구에서는 로(raw) 데이터인 웹 로그에서 유용한 패턴을 찾기 위한 전처리 과정을 알아보고, 웹 마이닝 시스템에 적합한 트랜잭션의 데이터 구조를 제시한다. 그리고 정의된 데이터 구조를 통한 패턴 발견 과정인 웹 사이트의 개념계층을 이용한 통계 기법과 연관규칙(Association Rules) 탐사에 대해 알아본다. 마지막으로 정의된 데이터 구조를 통한 새로운 유용한 패턴을 정의한ㄷ.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.53-63
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• 1998

Mining for association rules between items in a large database of sales transaction has been described as an important data mining problem. The mining of association rules can be mapped into the problem of discovering large itemsets. In this paper we present an efficient algorithm for mining association rules by minimizing the total numbers of candidate 2-itemset, │C$_2$│. More the total numbers of candidate 2-itemset, less the time of executing the algorithm for mining association rules. The total performance of algorithm depends on the time of finding large 2-itemsets. Hence, minimizing the total numbers of candidate 2-itemset is very important. We have performed extensive experiments and compared the performance of our algorithm with the DHP algorithm, the best existing algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.544-551
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• 2003

Internet as being generalized, intrusion detection system is needed to protect computer system from intrusions synthetically. We propose a criterion on profiling for intrusion detection system using anomaly detection. We present the cause of false positive on profiling and propose anomaly method to control this. Finally, we propose similarity function to decide whether anomaly action or not for user pattern using pattern database.