Algorithms for clustering data
Algorithms for clustering data
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Self-Organizing Maps
Machine Learning
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
An artificial bee colony approach for clustering
Expert Systems with Applications: An International Journal
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An ACO-based clustering algorithm
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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Data clustering has become one of the promising areas in data mining field. The algorithms, such as K-means and FCM are traditionally used for clustering purpose. Recently, most of the research studies have concentrated on optimisation of clustering process using different optimisation methods. The commonly used optimising algorithms such as particle swarm optimisation and genetic algorithms have given some significant contributions for optimising the clustering results. In this paper, we have proposed an approach to optimise the clustering process using artificial bee colony ABC algorithm with K-means operator. Here, we modify the traditional ABC algorithm with K-means operator. From the experimental results, we conclude that our proposed approach has upper hand over other methods. The comparative analysis of our approach with other algorithms using datasets such as iris, thyroid and wine is satisfactory. The proposed approach has achieved the intra-cluster distance values of 68.2, 9,682.4 and 12,234.4 for iris, thyroid and wine datasets respectively for the best cases.