A contemplate report on clustering evaluation and nonlinear clustering in high-dimensional data

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Every day people use large volumes of data, for future purpose data can be classified into different categories such as clusters. The main intension of the cluster is to divide unlabeled finite dataset in to different set of structures. Distribution of clusters classified into linearly independent clustering and non- linearly independent clustering. Non-linear independent clustering means at least one group with rounded boundaries or of arbitary figures. Many clustering algorithms don’t calculate approximately interior clusters. Several indexes used and planned for different Scenarios. There is no combining procedure for cluster assessment. We reconsider the existing clustering quality process and measure is difficult context designed for high-dimensional clustering. Dimensionality affect dissimilar clustering value indexes in dissimilar modes; few are preferred, to establish clustering quality in several ways. We are discuss in this paper, clustering evaluation, internal criteria, cluster quality indices, comparison of various clustering algorithms, problems in analyzing high dimensional data, clustering techniques for high dimensional data and perspectives and future directions.

     

     


  • Keywords


    Linear Clustering; Non- Linear Clustering; High-Dimensional Data; Hubness; Data Clustering; Cluster Indexes; Internal Indices; External Indices; Distance Concentration.

  • References


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Article ID: 19306
 
DOI: 10.14419/ijet.v7i3.29.19306




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