full
search.png

Search

close.png

Cancel

arrow
Volume 9, Issue 4
Enhanced K-Means Clustering Algorithm using A Heuristic Approach

Vighnesh Birodkar and Damodar Reddy Edla

J. Info. Comput. Sci. , 9 (2014), pp. 277-284.

Preview Purchase PDF 63 606

Cited by

google scholar semantic scholar
Export citation
  • Abstract
K-means algorithm is one of the most popular clustering algorithms that has been survived for more than 4 decades. Despite its inherent flaw of not knowing the number of clusters in advance, very few methods have been proposed in the literature to overcome it. The paper contains a fast heuristic algorithm for guessing the number of clusters as well as cluster center initialization without actually performing K-means, under the assumption that the clusters are well separated in a certain way. The proposed algorithm is experimented on various synthetic data. The experimental results show the effectiveness of the proposed approach over the existing.
  • Keywords

partitional clustering, K-means, unsupervised learning, cluster center, synthetic data

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JICS-9-277, author = {Vighnesh Birodkar and Damodar Reddy Edla}, title = {Enhanced K-Means Clustering Algorithm using A Heuristic Approach}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {9}, number = {4}, pages = {277--284}, abstract = {K-means algorithm is one of the most popular clustering algorithms that has been survived for more than 4 decades. Despite its inherent flaw of not knowing the number of clusters in advance, very few methods have been proposed in the literature to overcome it. The paper contains a fast heuristic algorithm for guessing the number of clusters as well as cluster center initialization without actually performing K-means, under the assumption that the clusters are well separated in a certain way. The proposed algorithm is experimented on various synthetic data. The experimental results show the effectiveness of the proposed approach over the existing. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22571.html} }
TY - JOUR T1 - Enhanced K-Means Clustering Algorithm using A Heuristic Approach AU - Vighnesh Birodkar and Damodar Reddy Edla JO - Journal of Information and Computing Science VL - 4 SP - 277 EP - 284 PY - 2024 DA - 2024/01 SN - 9 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22571.html KW - partitional clustering, K-means, unsupervised learning, cluster center, synthetic data AB - K-means algorithm is one of the most popular clustering algorithms that has been survived for more than 4 decades. Despite its inherent flaw of not knowing the number of clusters in advance, very few methods have been proposed in the literature to overcome it. The paper contains a fast heuristic algorithm for guessing the number of clusters as well as cluster center initialization without actually performing K-means, under the assumption that the clusters are well separated in a certain way. The proposed algorithm is experimented on various synthetic data. The experimental results show the effectiveness of the proposed approach over the existing.
Vighnesh Birodkar and Damodar Reddy Edla. (2024). Enhanced K-Means Clustering Algorithm using A Heuristic Approach. Journal of Information and Computing Science. 9 (4). 277-284. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard