• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.27-34
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• 2006

Recently, the study about the technology which effectively manage spatial information is actively conducted. For the effective knowledge inquiry, various extended data mining methods are applied in spatial data mining. However, former spatial association rule system appears the problem that does not reflect various non-spatial property along the inquiries because it searches the rule from the calculation among predicates. To resolve the problem, present study suggests the system that extends the inquiries using in spatial database, searches the association rule among non-spatial object property after setting the data based on space information. Especially, the model which is applicable to geographical information system is embodied. Embodied system with this method enables to search more useful spatial association rule in real life since it shows high migration property with extended spatial database and considers spatial property and various non-spatial property.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.105-108
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• 2005

본 논문에서는 지리정보 시스템인 GMS 를 기반으로 한 공간 연관 규칙의 구현과 설계 방법을 제안한다. GMS 에는 비공간 데이터와 공간 데이터가 테이블로 구분되어 저장되어 있다. 이를 이용하여 비공간 데이터 집합에서 관련된 데이터 집합을 추출한 후 그에 해당되는 공간 데이터를 이용하여 공간 연관 정보를 찾아내서 연관 규칙을 발견하는 방법에 대입하여 공간 연관 규칙을 발견한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.307-310
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• 2008

GIS 기술의 발달로 많은 양의 공간 데이터가 축적됨에 따라 공간 데이터 마이닝의 중요성이 커지고 있다. 본 논문에서는 새로운 공간 데이터 마이닝 시스템인 SD-Miner를 제안한다. SD-Miner는 크게 GUI 모듈과 데이터 마이닝 함수 모듈, 데이터 관리 모듈의 세부분으로 구성된다. GUI 모듈은 사용자의 입력과 출력을 담당한다. SD-Miner의 핵심 부분인 데이터 마이닝 함수 모듈은 공간 데이터 마이닝의 주요 기법인 공간 클러스터링, 공간 분류, 공간 특성화, 시공간 연관규칙 탐사 기능을 제공한다. 데이터 관리 모듈은 DBMS를 이용하여 데이터를 저장하고 관리한다. 실제 공간 데이터를 이용한 마이닝을 수행함으로써 개발된 SD-Miner의 실용성을 규명하고, 의미 있는 마이닝 결과들을 도출한다.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.105-115
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• 2007

Data mining extracts interesting knowledge from a large database. Among numerous data mining techniques, research work is primarily concentrated on clustering and association rules. The clustering technique of the active research topics mainly deals with analyzing spatial and attribute data. And, the technique of association rules deals with identifying frequent patterns. There was an advanced apriori algorithm using an existing bit-clustering algorithm. In an effort to identify an alternative algorithm to improve apriori, we investigated FP-Growth and discussed the possibility of adopting bit-clustering as the alternative method to solve the problems with FP-Growth. FP-Growth using bit-clustering demonstrated better performance than the existing method. We used chess data in our experiments. Chess data were used in the pattern mining evaluation. We made a creation of FP-Tree with different minimum support values. In the case of high minimum support values, similar results that the existing techniques demonstrated were obtained. In other cases, however, the performance of the technique proposed in this paper showed better results in comparison with the existing technique. As a result, the technique proposed in this paper was considered to lead to higher performance. In addition, the method to apply bit-clustering to GML data was proposed.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.119-132
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• 2009

Owing to the GIS technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. In this paper, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of three parts: a graphical user interface for inputs and outputs, a data mining module that processes spatial mining functionalities, a data storage model that stores and manages spatial as well as non-spatial data by using a DBMS. In particular, the data mining module provides major data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. SD-Miner has own characteristics: (1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively; (2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions. (3) It inputs parameters for mining as a form of database tables to increase flexibility. In order to verify the practicality of our SD-Miner developed, we present meaningful results obtained by performing spatial data mining with real-world spatial data.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.