An Efficient Hubness Clustering Model For High Dimensional Data

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-30 Number-2
Year of Publication : 2015
Authors : V.Geetha, G.Bharathi

MLA

V.Geetha, G.Bharathi "An Efficient Hubness Clustering Model For High Dimensional Data". International Journal of Computer Trends and Technology (IJCTT) V30(2):81-86, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
High dimensional data clustering can be seen in all fields these days and is becoming very tedious process. The important disadvantage of high dimensional data which we can give is that of the curse of dimensionality. As the magnitude of data sets grows the data points become sparse and density of the area becomes less making it difficult to cluster that data which further reduces the performance of traditional algorithms used for clustering .The organization maintains customer or product information in different forms which is difficult to perform clustering. Each data point has different in size and properties, but has to be clustered in meaningful and efficient way to get some knowledge from that. Many strategies have been proposed for clustering high dimensional data, but suffer with the problem of overlapping and retrieval efficiency. The proposed algorithm is basically used for increasing efficiency and accuracy.

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Keywords
Dataclustering, Sparsity ,Hubness, Nearest neighbours.