Open AccessHybrid data clustering approaches using bacterial colony optimization and k-means
Author(s) -
J. Revathi,
V. P. Eswaramurthy,
P. Padmavathi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1070/1/012064
Subject(s) - cluster analysis , cure data clustering algorithm , correlation clustering , canopy clustering algorithm , data mining , computer science , data stream clustering , determining the number of clusters in a data set , clustering high dimensional data , single linkage clustering , fuzzy clustering , algorithm , artificial intelligence
Data clustering is a fashionable data analysis technique in the data mining. K-means is a popular clustering technique for solving a clustering problem. However, the k-means clustering technique extremely depends on the initial position and converges to a local optimum. On the other hand, the bacterial colony optimization (BCO) is a well-known recently proposed data clustering algorithm. However, it is a high computational cost to complete a given solution. Hence, this research paper proposes a new hybrid data clustering method for solving data clustering problem. The proposed hybrid data clustering algorithm is a combination of the BCO and K-means called BCO+KM clustering algorithm. The experimental result shows that the proposed hybrid BCO+KM data clustering algorithm reveal better cluster partitions.