• Title/Summary/Keyword: spatial data mining

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Design and Implementation of a Spatial Data Mining System (공간 데이터 마이닝 시스템의 설계 및 구현)

  • Bae, DUck-Ho;Baek, Ji-Haeng;Oh, Hyun-Kyo;Song, Ju-Won;Kim, Sang-Wook;Choi, Myoung-Hoi;Jo, Hyeon-Ju
    • 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.

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Parallel Algorithm for Spatial Data Mining Using CUDA

  • Oh, Byoung-Woo
    • Journal of Advanced Information Technology and Convergence
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    • v.9 no.2
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    • pp.89-97
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    • 2019
  • Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

Mining Frequent Pattern from Large Spatial Data (대용량 공간 데이터로 부터 빈발 패턴 마이닝)

  • Lee, Dong-Gyu;Yi, Gyeong-Min;Jung, Suk-Ho;Lee, Seong-Ho;Ryu, Keun-Ho
    • Journal of Korea Spatial Information System Society
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    • v.12 no.1
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    • pp.49-56
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    • 2010
  • Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.

A Spatial Data Mining System Extending Generalization based on Rulebase (규칙베이스 기반의 일반화를 확장한 공간 데이터 마이닝 시스템)

  • Choi, Seong-Min;Kim, Ung-Mo
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.11
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    • pp.2786-2796
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    • 1998
  • Extraction of interesting and general knowledge from large spatial database is an important task in the development of geographical information system and knowledge-base systems. In this paper, we propose a spatial data mining system using generalization method; In this system, we extend an existing generalization mining and design a rulebase to support deriving new spatial knowledge. For this purpose, we propose an interleaved method which integrates spatial data dominated and nonspatial data dominated mining and construct a rulebase to extract topological relationship between spatial objects.

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Design and Implementation of an Open Object Management System for Spatial Data Mining (공간 데이타 마이닝을 위한 개방형 객체 관리 시스템의 설계 및 구현)

  • Yun, Jae-Kwan;Oh, Byoung-Woo;Han, Ki-Joon
    • Journal of Korea Spatial Information System Society
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    • v.1 no.1 s.1
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    • pp.5-18
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    • 1999
  • Recently, the necessity of automatic knowledge extraction from spatial data stored in spatial databases has been increased. Spatial data mining can be defined as the extraction of implicit knowledge, spatial relationships, or other knowledge not explicitly stored in spatial databases. In order to extract useful knowledge from spatial data, an object management system that can store spatial data efficiently, provide very fast indexing & searching mechanisms, and support a distributed computing environment is needed. In this paper, we designed and implemented an open object management system for spatial data mining, that supports efficient management of spatial, aspatial, and knowledge data. In order to develop this system, we used Open OODB that is a widely used object management system. However, the lark of facilities for spatial data mining in Open OODB, we extended it to support spatial data type, dynamic class generation, object-oriented inheritance, spatial index, spatial operations, etc. In addition, for further increasement of interoperability with other spatial database management systems or data mining systems, we adopted international standards such as ODMG 2.0 for data modeling, SDTS(Spatial Data Transfer Standard) for modeling and exchanging spatial data, and OpenGIS Simple Features Specification for CORBA for connecting clients and servers efficiently.

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A Spatial Analysis Supporting System Based On CRM And Data Mining Technique

  • Seo, Jeong-Min;Wei, Hu Xiao;Lee, Sang-Moon
    • Journal of Korea Multimedia Society
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    • v.12 no.6
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    • pp.777-784
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    • 2009
  • Recently, the importance of geoCRM (geographic Customer Relationship Management) systems are growing rapidly. So, result of the recognition that their applications extend well beyond the traditional CRM systems with the advent of ubiquitous environment and generalized location based services. A majority of traditional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. On the other hand, the geoCRM systems can be built as providing the geographic-based functions about CRM, including spatial and market analyses and the visualization of customer data, etc. However, it lacks the specific model and implementation of the geoCRM systems, being caused by the incomprehension of needs, the absence of related standards and the difficulties of development, and so on. In this paper, we develop a new spatial analysis supporting system that to enhance productivity through the convenient use and management of spatial data. The functionality provided by our system includes a set of analysis functions based on data mining techniques which allow a user to affect powerful transformation on spatial data. Particularly, both spatial data and non-spatial attributes can be efficiently handled as an object through our OODBMS.

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A Spatial Data Mining and Geographical Customer Relationship Management System (공간 데이터마이닝을 이용한 고객 관리시스템)

  • Lee, Sang-Moon;Seo, Jeong-Min
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.6
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    • pp.121-128
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    • 2010
  • Spatial data mining has been developed to support spatial association knowledge between spatial features or its non-spatial attributes for an application areas. At the present time, a number of researchers attempt to the data mining techniques apply to the several analysis areas, for examples, civil engineering, environmental, agricultural areas. Despite the efforts that, until such time as not existed practical systems for the gCRMDMs. gCRMDMs is merged with very large spatial database and CRM information system. Also, it is discovery the association rule for the predictions of customer's shopping pattern informations in a huge database consisted with spatial and non-spatial dataset. For this goal, gCRMDMs need spatial data mining techniques. But, nowadays, in a most case not exist utilizable model for the gCRMDMs. Therefore, in this paper, we proposed a practical gCRMDMs model to support a customer, store, street, building and geographical suited to the trade area.

An Efficient Grid Cell Based Spatial Clustering Algorithm for Spatial Data Mining (공간데이타 마이닝을 위한 효율적인 그리드 셀 기반 공간 클러스터링 알고리즘)

  • Moon, Sang-Ho;Lee, Dong-Gyu;Seo, Young-Duck
    • The KIPS Transactions:PartD
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    • v.10D no.4
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    • pp.567-576
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    • 2003
  • Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exists in spatial databases, is a challenging task due to the huge amounts of spatial data. Clustering algorithms are attractive for the task of class identification in spatial databases. Several methods for spatial clustering have been presented in recent years, but have the following several drawbacks increase costs due to computing distance among objects and process only memory-resident data. In this paper, we propose an efficient grid cell based spatial clustering method for spatial data mining. It focuses on resolving disadvantages of existing clustering algorithms. In details, it aims to reduce cost further for good efficiency on large databases. To do this, we devise a spatial clustering algorithm based on grid ceil structures including cell relationships.

A Study on Spatial Patterns of Traffic Accidents using GIS and Spatial Data Mining Methods: A Case Study of Kangnam-gu, Seoul (GIS와 공간 데이터마이닝을 이용한 교통사고의 공간적 패턴 분석 - 서울시 강남구를 사례로 -)

  • 이건학
    • Journal of the Korean Geographical Society
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    • v.39 no.3
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    • pp.457-472
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    • 2004
  • The purpose of this study is to analyze spatial patterns of traffic accidents and to investigate spatial relations among neighboring spatial objects by applying GIS and spatial data mining methods. This study investigated traffic accident data in Kangnam-gu, Seoul, as a case study. As a result, four clusters were emerged based on individual attributes of traffic accidents. Each cluster showed distinctive properties. In spatial associations between individual attributes of traffic accidents and neighboring spatial objects, there were many rules according to concept hierarchy and definition of spatial relations. Although all rules were not be interesting and significant, they could be a clue to investigate more.

Topic Model Analysis of Research Trend on Spatial Big Data (공간빅데이터 연구 동향 파악을 위한 토픽모형 분석)

  • Lee, Won Sang;Sohn, So Young
    • Journal of Korean Institute of Industrial Engineers
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    • v.41 no.1
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    • pp.64-73
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    • 2015
  • Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.