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基于层次迭代思想的聚类算法的研究 被引量:3

The Clustering Algorithm of Level Lterated Theory
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摘要 聚类分析是数据挖掘中的一个重要研究领域,是一种数据划分或分组处理的重要手段和方法。通过基于迭代思想的聚类算法,可对给定的数据对象集合进行层次分解,最终将样本空间分类成有聚类集合。 Cluster analysis is a major research field in data mining which also is an important means and method of data partitioning or grouping.Cluster algorithm can conduct the hierarchical decomposition of given data sets and finally classify the sample spatial assortments into clustering sets.
出处 《唐山学院学报》 2011年第3期86-87,91,共3页 Journal of Tangshan University
关键词 数据挖掘 聚类分析 层次算法 data mining cluster analysis hierarchical algorithm
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  • 1周水庚.DBSCAN算法的扩展技术.复旦大学计算机科学系技术报告[M].,1999,4..
  • 2Han JW, Kambr M. Data Mining Concepts and Techniques. Beijing: Higher Education Press, 2001. 145-176.
  • 3Kaufan L, Rousseeuw PJ. Finding Groups in Data: an Introduction to Cluster Analysis. New York: John Wiley & Sons, 1990.
  • 4Ester M, Kriegel HP, Sander J, Xu X. A density based algorithm for discovering clusters in large spatial databases with noise. In:Simoudis E, Han JW, Fayyad UM, eds. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining.Portland: AAAI Press, 1996. 226-231.
  • 5Guha S, Rastogi R, Shim K. CURE: an efficient clustering algorithm for large databases. In: Haas LM, Tiwary A, eds. Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998. "73-84.
  • 6Agrawal R, Gehrke J, Gunopolos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining application. In: Haas LM, Tiwary A, eds. Proceedings of the ACM SIGMOD International Conference on Management of Data.Seattle: ACM Press, 1998.94-105.
  • 7Alexandros N, Yannis T,Yannis M. C^2P: clustering based on closest pairs. In: Apers PMG, Atzeni P, Ceri S, Paraboschi S,Ramamohanarao K, Snodgrass RT, eds. Proceedings of the 27th International Conference on Very Large Data Bases. Roma:Morgan Kaufmann Publishers, 2001. 331-340.
  • 8Berchtold S, Bohm C, Kriegel H-P. The pyramid-technique: towards breaking the curse of dimensionality. In: Haas LM, Tiwary A,eds. Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle: ACM Press, 1998. 142- 153.
  • 9Yu C, Ooi BC, Tan K-L, Jagadish HV. Indexing the distance: an efficient method to KNN processing. In: Apers PMG, Atzeni P,Ceri S, Paraboschi S, Ramamohanarao K, Snodgrass RT, eds. Proceedings of the 27th International Conference on Very Large Data Bases. Roma: Morgan Kaufmann Publishers, 2001. 421--430.
  • 10周水庚,复旦大学计算机科学系技术报告,1999年

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