• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1998-2004
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• 2009

Recently, due to technical developments of various storage devices and networks, the amount of data increases rapidly. The large volume of data streams poses unique space and time constraints on the data mining process. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Most of the researches based on the support are concerned with the frequent itemsets, but ignore the infrequent itemsets even if it is crucial. In this paper, we propose an efficient method WSFI-Mine(Weighted Support Frequent Itemsets Mine) to mine all frequent itemsets by one scan from the data stream. This method can discover the closed frequent itemsets using DCT(Data Stream Closed Pattern Tree). We compare the performance of our algorithm with DSM-FI and THUI-Mine, under different minimum supports. As results show that WSFI-Mine not only run significant faster, but also consume less memory.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.741-754
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• 2008

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process regardless of the amount of information generated in the data stream. For this purpose, this paper proposes the Prime pattern tree(PPT) for finding frequent itemsets over data streams with using the confined memory space. Unlike a prefix tree, a node of a PPT can maintain the information necessary to estimate the current supports of several itemsets together. The length of items in a prime pattern can be reduced the total number of nodes and controlled by split_delta $S_{\delta}$. The size and the accuracy of the PPT is determined by $S_{\delta}$. The accuracy is better as the value of $S_{\delta}$ is smaller since the value of $S_{\delta}$ is large, many itemsets are estimated their frequencies. So it is important to consider trade-off between the size of a PPT and the accuracy of the mining result. Based on this characteristic, the size and the accuracy of the PPT can be flexibly controlled by merging or splitting nodes in a mining process. For finding all frequent itemsets over the data stream, this paper proposes a PPT to replace the role of a prefix tree in the estDec method which was proposed as a previous work. It is efficient to optimize the memory usage for finding frequent itemsets over a data stream in confined memory space. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.945-957
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• 2007

Quantitative association rules apply a binary association to the data which have the relatively strong quantitative attributions in a large database system. When a domain range of quantitative data which involve the significant meanings for the association is too broad, a domain requires to be divided into a proper interval which satisfies the minimum support for the generation of large interval items. The reliability of formulated rules is enormously influenced by the generation of large interval items. Therefore, this paper proposes a new method to efficiently generate the large interval items. The proposed method does not lose any meaningful intervals compared to other existing methods, provides the accurate large interval items which are close to the minimum support, and minimizes the loss of characteristics of data. In addition, since our method merges data where the frequency of data is high enough, it provides the fast run time compared with other methods for the broad quantitative domain. To verify the superiority of proposed method, the real national census data are used for the performance analysis and a Clunix HPC system is used for the parallel processing.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.471-477
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• 2018

As time changes, users change their interest. In this paper, we propose a method to provide suitable service for users by dynamically weighting service interests in the context of age, timing, and seasonal changes in ubiquitous environment. Based on the service history data presented to users according to the age or season, we also offer useful services by continuously adding the most recent service rules to reflect the changing of service interest. To do this, a set of services is considered as a transaction and each service is considered as an item in a transaction. And also we represent the association of services in a graph and extract frequent service items that refer to the latest information services for users.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.695-697
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• 2003

다중데이터베이스 마이닝에서 하나의 데이터 집합을 형성하는 작업은 많은 부하가 따른다. 그러므로, 본 논문에서는, 가중치 거리를 이용한 클러스터링을 통해 관련성이 높은 데이터베이스를 식별하는 기법을 제안한다. 제안한 기법은 빈발한 항목으로 구성된 데이터 집합을 생성하여 데이터베이스 사이의 유사성과 거리를 측정하고 데이터베이스간의 거리에 대한 식별성을 향상시키기 위하여 최다 빈발항목에 대한 비교 연산을 통해 가중치를 산출한다. 그리고 성능평가를 통하여 제안한 기법이 Ideal&Goodness 기법보다 다중데이터베이스의 트랜잭션 데이터베이스에 대한 식별 능력이 우수함을 알 수 있었다.

• 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.

• Patent21
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• s.93
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• pp.38-49
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• 2011

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.651-660
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• 2006

Algorithms for mining association rules based on the Apriori algorithm use the hash tree data structure for storing and counting supports of the candidate frequent itemsets and the most part of the execution time is consumed for searching in the hash tree. The DHP(Direct Hashing and Pruning) algorithm makes efforts to reduce the number of the candidate frequent itemsets to save searching time in the hash tree. For this purpose, the DHP algorithm does preparative simple counting supports of the candidate frequent itemsets. At this time, the DHP algorithm uses the direct hash table to reduce the overhead of the preparative counting supports. This paper proposes and evaluates an efficient hashing mechanism for the direct hash table $H_2$ which is for pruning in phase 2 and the hash tree $C_k$, which is for counting supports of the candidate frequent itemsets in all phases. The results showed that the performance improvement due to the proposed hashing mechanism was 82.2% on the maximum and 18.5% on the average compared to the conventional method using a simple mod operation.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.82-84
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• 2005

본 논문에서는 기존의 멀티미디어 연관 규칙 알고리즘인 Max occur 알고리즘에서 추출한 빈발 항목 집합의 결과들에 대하여 빈발 항목 집합들끼리의 공간적인 연관 관계를 고려하기 위챈 공간 데이터 마이닝의 대표적인 공간 분할 방법인 그리드 셀 기반으로 곰간 분할 지수(spatial facotr)인 SF를 이용한 이미지 공간 연관 규칙 마이닝 방법을 제시한다. 또한 최소 공간 지지도를 적용하여 이미지 데이터에서 반복적으로 발생하는 항목과 항목간의 공간 관계를 통해 이미지 연관 규칙을 마이닝 하는데 보다 유효한 알고리즘을 제안한다.