Survey on Clustering High-Dimensional data using Hubness

Authors

  • Miss. Archana Chaudahri  ME Scholar, Computer Engineering, GF's GCOE, Jalgaon, Jalgoan, Maharashtra, India
  • Mr. Nilesh Vani  Assistant Professor, Computer Engineering, GF's GCOE, Jalgon, Jalgoan, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT195671

Keywords:

Hubness, Clustering Methods, Datamining Techniques

Abstract

Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-01-05

Issue

Volume 6, Issue 1, January-February-2020

Section

Research Articles

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How to Cite

[1]
Miss. Archana Chaudahri, Mr. Nilesh Vani, " Survey on Clustering High-Dimensional data using Hubness" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 1, pp.01-07, January-February-2020. Available at doi : https://doi.org/10.32628/CSEIT195671