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研究生:劉建志
研究生(外文):Liu Chien Chih
論文名稱:一個以三角化為基礎的叢集分析方法
論文名稱(外文):A Triangulation-based Cluster Analysis Method
指導教授:陳春賢陳春賢引用關係
學位類別:碩士
校院名稱:長庚大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:90
中文關鍵詞:資料探勘叢集分析橋樑雜訊
外文關鍵詞:Data MiningCluster AnalysisBridgeNoise
相關次數:
  • 被引用被引用:0
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  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:3
本論文提出一個新的叢集分析方法,以Delaunay Diagram作為基礎,善用圖形中的三角形資訊,能夠過濾出雜訊(Noise),辨識出正確的叢集。本方法可辨識出緊臨高密度叢集的稀疏叢集與偵測出橋樑(Bridge)的存在,最後由圖形化的界面來顯示叢集的結果,並透過統計圖表揭露資料集的特性。叢集結果的好壞,常常受到資料集本身的特性所影響。如何揭露資料集的特性,並過濾出影響叢集結果的雜訊,也成為一個相當重要的議題。
This research proposes a new cluster analysis method, which is based on “Delaunay Diagram”. Comparing to other methods, in this method we use the information of the triangles in the diagram help identify correct clusters and recognize noises. This new cluster analysis method could also identify “sparse clusters” adjacent to high-density clusters and detect the existence of “bridges”. The result of clustering usually depends on the characteristic of the data set. How to expose the characteristic of the data set and recognize noises have become an important issue. So our method adopts the graphical interface to display the result of clustering, and uncovers the characteristic of the data set through statistic charts. Helping the users identify the correct clusters and discover the characteristic of the data set.
目 錄
誌謝……………………………………………………………………Ⅰ
中文摘要………………………………………………………………Ⅱ
英文摘要………………………………………………………………Ⅲ
目錄……………………………………………………………………Ⅳ
圖目錄…………………………………………………………………Ⅵ
表目錄…………………………………………………………………Ⅸ
第一章 緒論 ……………………………………………………………1
1.1研究背景與動機………………………………………………………1
1.2研究目的與限制………………………………………………………3
1.3論文架構………………………………………………………………4
第二章 文獻探討………………………………………………………6
2.1叢集技術………………………………………………………………7
2.1.1叢集過程………………………………………………………7
2.1.2切割式叢集演算法 …………………………………………11
2.1.3階層式叢集演算法 …………………………………………13
2.1.4密度基礎叢集演算法 ………………………………………17
2.1.5格子基礎叢集演算法 ………………………………………19
2.2 AMOEBA演算法………………………………………………………21
2.3 AUTOCLUST演算法 …………………………………………………27
2.4 Delaunay Diagram …………………………………………………30
2.5叢集分析所面對的問題 ……………………………………………32
第三章 新型的叢集分析演算法………………………………………35
3.1邊的分類 ……………………………………………………………35
3.2叢集方式 ……………………………………………………………37
3.3演算流程 ……………………………………………………………41
3.4參數設定 ……………………………………………………………48
第四章 驗證與分析……………………………………………………55
4.1測試資料集 …………………………………………………………55
4.2叢集結果 ……………………………………………………………57
第五章 結論……………………………………………………………76
參考文獻 ………………………………………………………………78
英文部份:
[1] Agrawal, R., Gehrke, J., Gunopulos, D., and Raghavan, P. “Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications,” In Int. Conf. on Management of Data, pp: 94-105, Seattle, Washington, 1998.
[2] Ankerst, M., Breunig, M., Kriegel, H.P., and Sander, J. “OPTICS: Ordering points to identify the clustering structure,” In Proc. 1999 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’99), pp: 49-60, Philadelphia, PA, June. 1999.
[3] Berg, M., Kreveld, M., Overmars, M., and Shwarzkopf, O. “COMPUTATIONAL GEOMETRY Algorithms and Applications,” John Wiley & Sons, 2nd edition, 2000.
[4] Ertoz, L., Steinbach, M., and Kumar, V. “Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data,” In Proc. of SIAM, 2003.
[5] Ester, M., Kriegel, H.P., Sander, J. and Xu, X. “Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” In Proc. 1996 Int. Conf. on Data Mining and Knowledge Discovery (DMKD’96), pp: 226-231, Portland, OR, Aug. 1996.
[6] Estivill-Castro, V. and Lee, I. “AMOEBA: Hierarchical Clustering Based on Spatial Proximity Using Delaunay Diagram,” In Proc. 9th Int. Spatial Data Handling (SDH2000), pp: 10-12, Beijing, China, Aug. 2000.
[7] Estivill-Castro, V. and Lee, I. “AUTOCLUST: Automatic Clustering via Boundary Extraction for Massive Point Data Sets,” In Proc. 5th Int. Conf. Geo-Computation, pp: 23-25, University of Greenwich, Kent, UK, Aug. 2000.
[8] Guha, S., Rastogi, R. and Shim, K. “CURE: An efficient clustering algorithm for large databases,” In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98), pp: 73-84, Seattle, WA, June 1998.
[9] Guha, S., Rastogi, R. and Shim, K. “ROCK: A Robust Clustering Algorithm For Categorical Attribute,” In Proc. 1999 Int. Conf. Data Engineering (ICDE’99), pp: 512-521, Sydney, Australia, Mar. 1999.
[10] Han, J. and Kamber, M. “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000.
[11] Han, J., Kamber, M., and Tung, A.K.H. “Spatial clustering methods in data mining: A survey,” In Geographic Data Mining and Knowledge Discovery, 2001
[12] Huang, Z. “Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values,” Data Mining and Knowledge Discovery (DMKD’98), Vol. 2, pp: 283-304, 1998.
[13] Jain, A.K., Murty, M.N., Flynn, P.J. “Data Clustering:A Review” ACM Computing Surveys, Vol. 31, No. 3, September 1999.
[14] Karypis, G., Han, E.H. and Kumar, V. “CHAMELEON: Hierarchical Clustering Using Dynamic Modeling,” IEEE Computer, Vol. 32, No. 8, pp: 68-75, 1999.
[15] Kaufman, L. and Rousseeuw, P.J. “Finding Groups in Data: an Introduction to Cluster Analysis,” John Wiley & Sons, 1990.
[16] KOLATCH, E. 2001. “Clustering Algorithms for Spatial Databases: A Survey.” PDF is available on the Web.
[17] MacQueen, J. “Some Methods for Classification and Analysis of Multivariate Observations,” In Proc. 5th Berkeley Symp. Math. Stat. and Prob., Vol. 1, pp: 281-297, 1967.
[18] Ng, R. and Han, J. “Efficient and Effective Clustering Method for Spatial Data Mining,” In Proc. 1994 Int. Conf. Very Large Databases (VLDB’94), pp: 144-155, Santiago, Chile, Sept. 1994.
[19] Openshaw, S. Geographical data mining: key design issues. In 99 Proceedings of GeoComputation, 1999.
[20] Pavel, B. “Survey of Clustering Data Mining Techniques.” Accrue Software, 2002.
[21] Qian, Y., Zhang, G., and Zhang, K. “FAÇADE: A Fast and Effective Approach to the Discovery of Dense Clusters in Noisy Spatial Data,” In Proc. ACM SIGMOD 2004 Conference, Paris, France, 13-18 June 2004, ACM Press.
[22] Sheikholeslami, G., Chatterjee, S., and Zhang A. “WaveCluster: A multi-resolution clustering approach for very large spatial databases,” In Proc. 1998 Int. Conf. Very Large Databases (VLDB’98), pp: 428-439, New York, Aug. 1998.
[23] Wang, W., Yang, Z. and Muntz, R. “STING: A Statistical Information grid Approach to Spatial Data Mining,” In Proc. 1997 Int. Conf. Very Large Data Bases(VLDB’97), pp: 186-195, Athens, Greece, Aug. 1997.
[24] Yu, Q. and Kang, Z. “Discovering Spatial Patterns Accurately with Effective Noise Removal,” Data Mining and Knowledge Discovery (DMKD’04), Paris, France, pp: 43-50, June 2004.
[25] Zhang, T., Ramakrishnan, R. and Livny, M. “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” In Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’96), pp: 103-114, 1996.

中文部份:
[26] 林育臣, “A Study of Clustering Technology,” 朝陽科技大學, 資訊管理研究所, 碩士論文, 2002.
[27] 張紘愷, ”Study of Clustering Techniques Applied to Data Minig,”高雄應用科技大學, 資訊管理研究所, 碩士論文, 2004.
[28] 麥可‧斐瑞和戈登‧林諾夫著, 彭文正譯, “Data Mining 資料採礦 顧客關係管理暨電子行銷之應用,” 數博網資訊股份有限公司, 2001.
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