An improved algorithm to handle noise objects in the process of clustering

Hasanthi A. Pathberiya, Chandima D. Tilakaratne and Liwan L. Hansen

International Journal of Data Science, 2019, vol. 4, issue 1, 1-17

Abstract: Cluster analysis is considered as an approach for unsupervised learning. It tends to recognise hidden grouping structure in a set of objects using a predefined set of rules. Objects occupying unusual characteristics add noise to the data space. As a result, complexities and misinterpretation in clustering structures will arise. This study aims at proposing a novel iterative approach to eradicate the effect of noise objects in the process of deriving clusters of data. Performance of the proposed approach is tested on partitioning, hierarchical and neural network based clustering algorithms using both simulated and standard datasets supplemented with noise. An improvement in the quality of clustering structure resulted from the proposed approach is witnessed, compared to that of conventional clustering algorithms.

Keywords: clustering algorithms; handling noise data; mining methods and algorithms; k-means; Ward's method; self organising map. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=98358 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:4:y:2019:i:1:p:1-17

Access Statistics for this article

More articles in International Journal of Data Science from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().