• [email protected]
  • +971 507 888 742
Submit Manuscript
SciAlert
  • Home
  • Journals
  • Information
    • For Authors
    • For Referees
    • For Librarian
    • For Societies
  • Contact
  1. Information Technology Journal
  2. Vol 10 (6), 2011
  3. 1092-1105
  • Online First
  • Current Issue
  • Previous Issues
  • More Information
    Aims and Scope Editorial Board Guide to Authors Article Processing Charges
    Submit a Manuscript

Information Technology Journal

Year: 2011 | Volume: 10 | Issue: 6 | Page No.: 1092-1105
DOI: 10.3923/itj.2011.1092.1105

Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend



Total views 1250

Search


Authors


Singh Vijendra

Country: India

Keywords


  • high dimensional data
  • Subspace clustering
  • density based clustering
  • Feature selection
Review Article

Efficient Clustering for High Dimensional Data: Subspace Based Clustering and Density Based Clustering

Singh Vijendra
Finding clusters in a high dimensional data space is challenging because a high dimensional data space has hundreds of attributes and hundreds of data tuples and the average density of data points is very low. The distance functions used by many conventional algorithms fail in this scenario. Clustering relies on computing the distance between objects and thus, the complexity of the similarity models has a severe influence on the efficiency of the clustering algorithms. Especially for density-based clustering, range queries must be supported efficiently to reduce the runtime of clustering. The density-based clustering is also influenced by the density divergence problem that affects the accuracy of clustering. If clusters do not exist in the original high dimensional data space, it may be possible that clusters exist in some subspaces of the original data space. Subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. Subspace clustering algorithms identifies such subspace clusters. But for clustering based on relative region densities in the subspaces, density based subspace clustering algorithms are applied where the clusters are regarded as regions whose densities are relatively high as compared to the region densities in a subspace. This study presents a review of various subspaces based clustering algorithms and density based clustering algorithms with their efficiencies on different data sets.
PDF Fulltext XML References Citation

How to cite this article

Singh Vijendra, 2011. Efficient Clustering for High Dimensional Data: Subspace Based Clustering and Density Based Clustering. Information Technology Journal, 10: 1092-1105.

DOI: 10.3923/itj.2011.1092.1105

URL: https://scialert.net/abstract/?doi=itj.2011.1092.1105

Related Articles

An Intelligent Mining Framework based on Rough Sets for Clustering Gene Expression Data
A Fast Evolutionary Algorithm for Automatic Evolution of Clusters
A Complete Survey of Duplicate Record Detection Using Data Mining Techniques
A Framework for Classifying Uncertain and Evolving Data Streams
Variations of k-mean Algorithm: A Study for High-Dimensional Large Data Sets
Clustering Methods for Statistical Analysis of Genome Databases
A Robust Algorithm for Subspace Clustering of High-Dimensional Data*
Integrated Approach of Reduct and Clustering for Mining Patterns from Clusters
A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach

Leave a Comment


Your email address will not be published. Required fields are marked *

Useful Links

  • Journals
  • For Authors
  • For Referees
  • For Librarian
  • For Socities

Contact Us

Office Number 1128,
Tamani Arts Building,
Business Bay,
Deira, Dubai, UAE

Phone: +971 507 888 742
Email: [email protected]

About Science Alert

Science Alert is a technology platform and service provider for scholarly publishers, helping them to publish and distribute their content online. We provide a range of services, including hosting, design, and digital marketing, as well as analytics and other tools to help publishers understand their audience and optimize their content. Science Alert works with a wide variety of publishers, including academic societies, universities, and commercial publishers.

Follow Us
© Copyright Science Alert. All Rights Reserved