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Student Number 974203024
Author Hsin-I Tseng(´¿¤ß©É)
Author's Email Address No Public.
Statistics This thesis had been viewed 440 times. Download 86 times.
Department Information Management
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language English
Title A Two-Attributes-Set Spatial Clustering Algorithm for Geographic Data
Date of Defense 2010-06-21
Page Count 61
Keyword
  • Cluster analysis
  • Data mining
  • Spatial Clustering
  • Abstract Cluster analysis has recently become a highly active topic in data mining research. However, traditional clustering algorithms had a restriction that they consider only one set of attributes. Actually, we can divide all attributes of a spatial object into two attribute sets. For example, Weather Bureau would like to know which regions have similar climate phenomenon, where each weather station are described by latitude and longitude attributes, and measurement of temperature, precipitation attributes. Therefore, two different attribute sets are required for spatial clustering, where one set is spatial attributes and the other one is characteristic attributes. Traditional algorithms do not distinguish the two sets of attributes, which lead to low quality spatial clustering results. We propose Two-Attributes-Set Spatial Clustering, generating clusters that can be segmented by characteristic attributes and objects in the same cluster are similar in spatial attributes as well.
    Table of Content Chapter 1 Introduction1
    1.1 Background1
    1.2 Motivation2
    1.3 Research Objectives5
    1.4 Thesis Framework5
    Chapter 2 Related Works6
    2.1 Cluster spatial objects without characteristic attributes7
    2.2 Cluster spatial objects with characteristic attributes9
    Chapter 3 The Problem and the Definitions13
    3.1 Research Problem13
    3.2 Definitions14
    Chapter 4 Two-Attributes-Set Spatial Clustering24
    4.1 Overview of the Algorithm24
    4.2 The Clustering Algorithm27
    4.2.1 Parameters of the Algorithm27
    4.2.2 Procedure of the Algorithm28
    4.2.3 Algorithm29
    4.3 Example33
    Chapter 5 Experiments39
    5.1 Content of Experiments39
    5.2 Performance Evaluation40
    5.2.1 Indicators for clustering results measures40
    5.2.2 Parameter Optimization43
    5.2.3 Clustering results measures49
    5.2.4 Draw the Clustering results53
    Chapter 6 Conclusions59
    6.1 Implications for Academic Researches59
    6.2 Implications for Business Practitioners60
    6.3 Future Works60
    References61
    Reference [1]Ester, M., Kriegel, H. P. and Sander, J., 1997, ¡§Spatial Data Mining: A Database Approach,¡¨ Proc. Fifth International Symposium on Large Spatial Databases, pp. 48-66.
    [2]Ng, R. T. and Han, J., 1994, ¡§Efficient and Effective Clustering Method for Spatial Data Mining,¡¨ International Conference Very Large Databases, pp. 144-155.
    [3]Koperski, K. and Han, J., 1995, ¡§Discovery of Spatial Association Rules in Geographic Information Database,¡¨ In Advances in Spatial Databases, Vol. 4, pp. 47-66.
    [4]Chawla, S., Shekhar, S., WU, W. and Ozesmi, U., 2000, ¡§Extending Data Mining for Spatial Application: A Case Study in Predicting Nest Locations,¡¨ ACM SIGMOD Workshop on Research Issue in Data Mining and Knowledge Discovery.
    [5]Han, J. and Kamber, M., 2000, ¡§Data Mining: Concepts and Techniques,¡¨ Morgan Kaufmann.
    [6]Jain, A. K., Murty, M. N. and Flynn, P.J., 1999, ¡§Data Clustering: A Review,¡¨ ACM Computing Surveys, Vol. 31, No. 3, pp. 264-323.
    [7]Liu, B., Xia, Y. and Yu, P., 2000, ¡§Clustering Through Decision Tree Construction,¡¨ In SIGMOD. 
    [8]Cheng, C.H., Fu, A.W. and Zhang, Y., 1999, ¡§Entropy-Based Subspace Clustering for Mining Numerical Data,¡¨ KDD, pp. 84-93.
    [9]Ester, M., Kriegel, H. and Sander, J., 1999, ¡§Knowledge discovery in spatial databases,¡¨ German Conference on Artificial Intelligence, Vol. 23.
    [10]Singh, V. P., 1971, ¡§Model Flow Duration and Stream Variability,¡¨ Water  Resource Research, Vol. 7, No. 4, pp. 1031-1036. 
    [11]Mosley, M. P., 1981, ¡§Delimitation of New Zealand Hydrological Regions,¡¨ Journal of Hydrology, Vol. 49, pp. 173-92.
    [12]Wiltshire, S. E., 1986, ¡§Identification of Homogeneous Regions for flood frequency analysis,¡¨ Journal of Hydrology, Vol. 84, pp. 287-302.
    [13]Chen, C.-J., 2005, ¡§A Study On Clustering Characteristics of Spatial Distribution of Extreme Rainfall¡¨ Journal of Technology, Vol. 20, pp. 377-386.
    [14]Chen, H.-I, 2001, ¡§Defining the Housing Submarkets in Taipei Metropolitan Area¡¨ Master's Thesis of Department of Urban Planning, NCKU.
    [15]Hsu, L. H., and H. L., 2004, ¡§A Study on the Changing Spatial Patterns of the Retail-Service Industries in the Taichung Metropolitan Area,¡¨ Journal of Building and Planning National Taiwan University, Num. 11, pp. 1-19.
    [16]Karypis, G. Han, E-H., and Kumar, V., 1999, ¡§Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling,¡¨ IEEE Computer, pp. 68-75.
    [17]Guha, S., Rastogi, R., and Shim, K., 1999, ¡§ROCK: A Robust Clustering Algorithm for Categorical Attributes,¡¨ Int'l Conference on Data Engineering, pp. 512-521.
    [18]Guha, S., Rastogi, R., and Shim, K., 1998, ¡§CURE: an efficient clustering algorithm for large databases,¡¨ ACM SIGMOD International Conference on Management of Data, pp. 73-84.
    [19]http://cdc.cma.gov.cn/index.jsp
    [20]http://worldweather.wmo.int/index.htm
    [21]http://www.dgbas.gov.tw/ct.asp?xItem=18472&ctNode=3279
    [22]Kaufman, L., and Rousseeuw, P., 1990, ¡§Finding Groups in Data: An Introduction to Cluster Analysis,¡¨ Wiley.
    [23]Jung, C.-T., 2005, ¡§Spatial Data Mining on Census Data- A Case Study for Location Analysis of Convenience Stores in Taipei City¡¨ Journal of Taiwan Geographic Information Science, Vol. 2, pp. 45-56.
    [24]Ester, M., Frommelt, A., Kriegel, H.P. and Sander, J., 1998, ¡§Algorithms for Characterization and Trend Detection in Spatial Databases,¡¨ International Conference on Knowledge Discovery and Data Mining, Vol. 4, pp. 44-50.
    [25]Han, J., Kamber, M., and Tung, A. K. H., 2001, ¡§Spatial clustering methods in data mining: A survey.¡¨ In Miller, H. and Han, J., editors, In Geographic Data Mining and Knowledge Discovery, pp. 187¡V217.
    Advisor
  • Yen-Liang Chen(³¯«Û¨})
  • Files
  • 974203024.pdf
  • approve in 3 years
    Date of Submission 2010-07-14

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