| Title: |
Validity Index for Clustered Data in Non-negative Space |
| Authors: |
Modak, Soumita |
| Source: |
Calcutta Statistical Association Bulletin ; volume 75, issue 1, page 60-71 ; ISSN 0008-0683 2456-6462 |
| Publisher Information: |
SAGE Publications |
| Publication Year: |
2023 |
| Description: |
We propose a novel nonparametric cluster validity index which can be used to evaluate the unknown number of existing clusters prevailing a data set, to assess the quality of classification for a clustered set of data members, or to compare the clustering output obtained from different algorithms. Our efficient measure depends only on the observation-wise distances of the non-negative clustered data from their origin given in an arbitrary dimensional space. Its fast implementation makes it appealing for big data analysis, whereas the high-dimensional applicability widens its usefulness. Easy interpretation, simple algorithm, speedy computation and great performance, shown in terms of data study, establish our advised validity index as a strong cluster accuracy measure among the acknowledged ones from the literature. AMS subject classification: 62H30 |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1177/00080683231172377 |
| Availability: |
https://doi.org/10.1177/00080683231172377; http://journals.sagepub.com/doi/pdf/10.1177/00080683231172377; http://journals.sagepub.com/doi/full-xml/10.1177/00080683231172377 |
| Rights: |
http://journals.sagepub.com/page/policies/text-and-data-mining-license |
| Accession Number: |
edsbas.FAFAC03B |
| Database: |
BASE |