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  Enhancing K-Means Clustering with Bio-Inspired Algorithms  
  Authors : Doaa Abdullah; Hala Abdel-Galil; Ensaf Hussein
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Data clustering is considered an important data analysis and data mining technique. It is included in a variety of disciplines such as machine learning, pattern recognition and bioinformatics. K-Means algorithm is a popular clustering algorithm but it suffers from its dependency on its initial centroid locations which fells the algorithm into the local optima. Bio-inspired algorithms are powerful in searching for the global optimal solutions. In this paper, the most recent bio-inspired algorithms; Crow search, Whale optimization, Grasshopper optimization and Salp swarm algorithms are integrated into the K-Means algorithm, to overcome the K-Means drawbacks. The proposed techniques are implemented and applied on eight numerical UCI datasets. Experimental results reveal the capability of the proposed algorithms to find the optimal initial centroid locations which achieve better clustering integrity. Moreover, the results show that the integration of the k-Means with the Crow search algorithm is superior compared to the others bio-inspired algorithms.


Published In : IJCSN Journal Volume 7, Issue 6

Date of Publication : December 2018

Pages : 361-373

Figures :12

Tables : 14


Doaa Abdullah Abdel-Mohsen : is a teaching assistant in Computer Science department, Faculty of Computers and Information, Helwan University, Cairo, Egypt. She holds a bachelor in Computer Science with honors degree.

Dr. Hala Abdel-Galil : is associate professor of Computer Science, and head of the Computer Science Department, Faculty of Computers and information, Helwan University, Cairo, Egypt. She has skills and expertise in Image Processing, Pattern Recognition, Classification, Neural Networks and Artificial Intelligence, Computational Intelligence, Pattern Classification, Applied Artificial Intelligence and Machine Intelligence.

Dr. Ensaf Hussein Mohamed : received her Ph.D. in Computer Science, Faculty of Computers and Information, Helwan University, Cairo, Egypt, 2013. Her recent Research focuses on Natural Language Processing, Text Mining, and Machine Learning. Currently, she is an assistant professor, Faculty of Computers and Information, Helwan University, Cairo, Egypt.


Crow Search Algorithm, Whale optimization Algorithm, Salp Swarm algorithm, Grasshopper Optimization Algorithm, K-Means Clustering Algorithm, Sum of Squared Errors (SSE).

In this paper, we presented an integration of the k-Means algorithm with each one of the most recent bio-inspired algorithms to overcome the drawback of the K-means algorithm which is falling in the local optima and to maximize clusters integrity. C-Crow search algorithm, C- Salp algorithm, C-Whale search algorithm and CGrasshopper optimization algorithm are proposed and validated over eight datasets. six different evaluation criteria are adopted in this study. These criteria are the best, the worst, and the mean fitness value, the mean rank, the SD, and the Accuracy. The experimental results show that the proposed algorithms outperform the standard Kmeans algorithm in terms of the best, the worst and the mean fitness value.

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