Erratum: To finding groups in data: Cluster analysis with ants (Applied Soft Computing Journal (2009) 9:1 (61-70) DOI: 10.1016/j.asoc.2013.07.012)

Authors: Boryczka, U. and Budka, M.

Journal: Applied Soft Computing Journal

Volume: 13

Issue: 10

Pages: 4229

ISSN: 1568-4946

DOI: 10.1016/j.asoc.2008.03.002

Source: Scopus

Finding groups in data: Cluster analysis with ants

Authors: Boryczka, U. and Budka, M.

Journal: Applied Soft Computing

Volume: 9

Issue: 1

Pages: 61-70

ISSN: 1568-4946

DOI: 10.1016/j.asoc.2008.03.002


Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques.

Source: Manual

Preferred by: Marcin Budka