Conceptual ClusteringAnalysis in Data Mining: A Study

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Clustering on unsupervised learning handles with instances, which are not classified already and not having class attribute with them. Applying algorithms is to find useful but items on unknown classes. Approach of unsupervised learning is about instances are automatically making into meaningful groups basing on its similarity. This paper we study about the basic clustering       methods in data mining on unsupervised learning such as ensembles distributed clustering and its algorithms.

     

     


  • Keywords


    Clustering; Data Mining; Density-based; Hierarchical; k-means; Dendrogram.

  • References


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      [3] H. Ayad and M. Kamel. “Basing on sharing nearest neighbor finding natural clusters using multi-clusters combination”. In Multiple Classier Systems: Fourth International Workshop, MCS 2003, Guildford, Surrey, UK

      [4] M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander. Lof: “Identifying density-based local outliers.” In Proc. of SIGMOD’2000, pages 93–104, 2000.

      [5] Cheeseman P. & Stutz J., (1996), Bayesian Classification (AutoClass): Theory and Results, In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smith, and R. Uthurusamy, editors, “Advances in Knowledge Discovery and Data Mining.”

      [6] Yiu-Ming Cheung, k*-Means: “A new generalized k-means clustering algorithm, Pattern Recognition” Letters 24 (2003).

      [7] Hans-Peter Kriegel, Martin Pfeifle ,”Hierarchical Density-Based Clustering of Unsupervised Data” Institute for Computer Science University of Munich, Germany.

      [8] Hans-Peter Kriegel Martin Pfeifle “Density-Based Clustering of Uncertain Data.” University of Munich, Germany University of Munich, Germany Institute for Computer Science Institute for Computer Science.

      [9] Clustering of time series data—a survey T. Warren Liao∗Industrial & Manufacturing Systems Engineering Department, Louisiana State University, 3128 CEBA, Baton Rouge, LA 70803, USA.


 

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Article ID: 20465
 
DOI: 10.14419/ijet.v7i4.6.20465




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