Detail publikace

Exploring k-PSO Algorithm for Clustering

HERMAN, D. ORSÁG, F.

Originální název

Exploring k-PSO Algorithm for Clustering

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Cluster analysis is a very popular approach to fully automatic search for patterns, data concepts, groups and clusters. It simplifies data representations  and thus plays an important role in the process of knowledge acquisition. Data mining tasks require fast and accurate partition of data with many attributes. This requires new approach, which could deal better with these features. Methods based on the swarm intelligence present such approach to the cluster analysis. This article is a brief introduction to the optimization algorithms inspired by the natural world. It shows how these algorithms can be used in the cluster analysis and describes several up-to-date hybrid techniques combining PSO and k-means. Moreover, conceptually new hybrid algorithm based on the PSO and k-means is introduced and its efficiency and robustness are compared to the other algorithms using several datasets.

Klíčová slova

Swarm Intelligence, Clustering, SI, k-means, FCM, Exploring k-PSO

Autoři

HERMAN, D.; ORSÁG, F.

Rok RIV

2013

Vydáno

13. 2. 2013

Nakladatel

ACTA Press

Místo

Innsbruck

ISBN

978-0-88986-943-1

Kniha

Proceedings of the IASTED International Conference Artificial Intelligence and Applications (AIA 2013)

Strany od

161

Strany do

168

Strany počet

8

BibTex

@inproceedings{BUT103428,
  author="David {Herman} and Filip {Orság}",
  title="Exploring k-PSO Algorithm for Clustering",
  booktitle="Proceedings of the IASTED International Conference Artificial Intelligence and Applications (AIA 2013)",
  year="2013",
  pages="161--168",
  publisher="ACTA Press",
  address="Innsbruck",
  isbn="978-0-88986-943-1"
}