• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.316-318
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• 2000

데이터마이닝 기능은 문서의 구조화되지 않은 텍스트보다는 테이블과 일반적인 DB에 있는 구조화된 자료에 초점이 맞춰져 있다. 정보화의 과정속에서 많은 기업이나 조직들은 과거의 시스템을 DB로 구축하여 어느 정도 형태를 갖추게 되었지만, E-business, E-commerce가 활발해지면서 보유하고 있는 DB기반이 아닌 무작위의 새로운 데이터가 사용자들에 의해 생성되기도 한다. 본 논문에서는 이러한 텍스트 문서에 숨어있는 정보들을 발견하기 위한 텍스트마이닝 과정을 시나리오로 설정하고, 문서와 문서집합에 대해 분석도구를 적용하는 어플리케이션을 구현해 보았다. 대규모의 문서집합에 분석도구를 이용함으로써 빠른 문서처리가 가능하고 이는 사용자가 많은 양의 문서들을 다룰 때의 시간비용을 최소화시킬 수 있는 방법이 될 수 있다. 또한 마이닝과정을 통해 발견한 지식과 특징들을 기반으로 반구조화된 파일로 변환하여, 규칙발견, 데이터마이닝기법을 적용하여 의미있는 새로운 결론을 얻을 수 있을 것이다.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1511-1519
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• 2000

Inthis paper, we present an efficient migration strategy of mobile agent for data mining application. The purpose of the proposed algorithm is to set up the best migration plan of mobile agent with regard to minimizing network execution time .In order to verify the effectiveness of the proposed algorithm, we designed a performance evaulation model for three paradigms from data mining, i.e. RPC, mobile agent and mobile agent with locker pattern, and we then evaluated the algorithm by simulation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.135-137
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• 2012

A existing recommedation system using association rules has the problem, which is suffered from inefficiency by reprocessing of the data which have already been processed in the incremental data environment in which new data are added persistently. We propose the recommendation technique using incremental frequent pattern mining based on RFM in e-commerce. The proposed can extract frequent items and create association rules using frequent patterns mining rapidly when new data are added persistently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.955-959
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• 2004

Existing Clustering Methods for spatial data mining process only Point objects, not spatial objects with polygonometry such as lines and areas. It is because that distance computation between objects with polygonometry for clustering is more complex than distance computation between point objects. To solve this problem, we design a clustering method based on regular grid cell structures. In details, it reduces cost and time for distance computation using cell relationships in grid cell structures.

• Annual Conference of KIPS
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• 2001.10a
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• pp.31-34
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• 2001

대용량의 데이터들로부터 사용자가 원하는 데이터를 찾기 위하여 많은 데이터 마이닝 기술들이 연구되어 실제 응용프로그램에서 많이 적용되고 있다. 이러한 데이터 마이닝의 기술 중 연관규칙은 항목들의 집합으로 표현되는 트랜잭션에서 각 항목간의 연관성을 찾는데 사용된다. 그러나 실세계에는 트랜잭션이 없이 일련의 이벤트만 시간에 따라서 발생하는 데이터들이 많이 존재한다. 이러한 시계열 이벤트 데이터들로부터 다양한 가상 트랜잭션을 생성하는 기법들을 제시한다. 이러한 가상 트랜잭션 데이터로 변환된 시계열 데이터에 연관규칙, 순차패턴, 주기패턴과 관련된 여러 가지 알고리즘을 바로 적용 함으로서 유용한 규칙들을 발견해 낼 수 있다.

• Annual Conference of KIPS
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• 2008.11a
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• pp.375-378
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• 2008

오늘날 지식을 기반으로 하는 고도의 정보사회로 나아가는 시점에서 우리는 대량의 데이터 속에서 필요한 지식을 찾아내는 것에 초점을 모으게 되었다. 따라서 대량의 데이터 속에서 필요한 지식을 자동으로 찾아내는 데이터 마이닝에 대한 연구가 활발히 진행되고 있다. 데이터 마이닝은 대용량의 데이터를 대상으로 하기 때문에 정확도뿐만이 아니라 소요시간도 중요하기 때문에 성능 향상을 위한 알고리즘들이 많이 개발되었다. 데이터 마이닝의 성능을 향상시키기 위해서 가장 좋은 방법이 데이터베이스의 스캔의 횟수를 줄이는 것이다. 본 논문에서는 연관 규칙 탐사에서 빈발 항목 집합을 찾아내는 부분을 이진 표현을 이용하여 좀 더 성능을 향상시킬 수 있는 알고리즘을 제안한다.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.289-294
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• 2014

In the recent enterprises and are utilizing the CRM using data mining techniques and new marketing plan. However, data mining techniques are necessary expertise, general public access is difficult, it will be subject to constraints of time and space. in this paper, in order to solve this problem, we have proposed a Mobile CRM applying the data mining method. Thus, to analyze the structure of an existing CRM system, and defines the data flow and format. Also, define the process of the system, was designed sales trend analysis algorithm and customer sales recommendation algorithm using data mining techniques. Evaluation of the proposed system, through the test scenario to ensure proper operation, it was carried out the comparison and verification with the existing system. Results of the test, the value of existing programs and data matches to verify the reliability and use queries the proposed statistical tables to reduce the analysis time of data, it was verified rapidity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.9
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• pp.4183-4191
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• 2011

Considering the characteristics of a data stream whose data elements are continuously generated and may change over time, there have been many techniques to differentiate the importance of data elements in a data stream by their generation time. The conventional techniques are efficient to get an analysis result focusing on the recent information in a data stream, but they have a limitation to differentiate the importance of information in various ways more flexible. An information differentiation technique based on the term of a fuzzy set can be an alternative way to compensate the limitation. A term of a fuzzy set has been widely used in various data mining fields, which can overcome the sharp boundary problem and give an analysis result reflecting the requirements in real world applications more. In this paper, a fuzzy window mechanism is proposed, which is adapting a term of a fuzzy set and is efficiently used to differentiate the importance of information in mining data streams. Basic concepts including fuzzy calendars are described first, and subsequently details on data stream mining of weighted patterns using a fuzzy window technique are described.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.125-132
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• 2014

Since frequent pattern mining was proposed in order to search for hidden, useful pattern information from large-scale databases, various types of mining approaches and applications have been researched. Especially, frequent graph pattern mining was suggested to effectively deal with recent data that have been complicated continually, and a variety of efficient graph mining algorithms have been studied. Graph patterns obtained from graph databases have their own importance and characteristics different from one another according to the elements composing them and their lengths. However, traditional frequent graph pattern mining approaches have the limitations that do not consider such problems. That is, the existing methods consider only one minimum support threshold regardless of the lengths of graph patterns extracted from their mining operations and do not use any of the patterns' weight factors; therefore, a large number of actually useless graph patterns may be generated. Small graph patterns with a few vertices and edges tend to be interesting when their weighted supports are relatively high, while large ones with many elements can be useful even if their weighted supports are relatively low. For this reason, we propose a weight-based frequent graph pattern mining algorithm considering length-decreasing support constraints. Comprehensive experimental results provided in this paper show that the proposed method guarantees more outstanding performance compared to a state-of-the-art graph mining algorithm in terms of pattern generation, runtime, and memory usage.