Abstract
In data mining or machine learning clustering is very broad area. Clustering is a technique which decomposes the data set into different cluster. There are many clustering algorithms but k-mean algorithm is most popular and widely used in many fields such as image processing, machine learning, pattern reorganization etc.; but it has a major drawback that is; its output is really sensitive to the random selection of initial centroids or its final output is totally depends on initial selection of centroids. Because of this drawback many techniques were introduced for K-mean algorithm. This paper introduces an initial centroid selection method for K-mean algorithm by using Atkinson Index. Atkinson index is a technique for measuring the inequality here. It is used for initial seed selection; experimental result shows that the proposed technique, which we applying on liver patient data set gives more accurate result than the original K-mean algorithm.
Similar content being viewed by others
References
Aldahdooh RT, Ashour W (2013) DIM K-means—distance-based initialization method for K-means clustering algorithm. Int J Intell Syst Appl (IJISA) 5:41
Arai K, Barakbah A R (2007) Hierarchical K-means: an algorithm for centroids initialization for K-means, Reports of the Faculty of Science and Engineering, Saga Univ. Saga University
Baswade A M, Nalwade P S (2013) Selection of initial centroids for k-means algorithm. Int J Comput Sci Mob Comput
El Agha M, Ashour WM (2012) Efficient and Fast Initialization Algorithm for K-means Clustering. Int J Intell Syst Appl 4:21
Haiyan Z, Jianyang Z (2012) A new K-means initial cluster class of the center selection algorithm based on the great graph. Commun Inf Sci Manag Eng
Hameed M A, Ramachandram S., Jadaan O A. (2011) Information gain clustering through roulette wheel genetic algorithm (IGCRWGA) : a novel heuristic approach for personalization of cold start problem. In: International conference on computational intelligence and communication networks (CICN), IEEE
Karegowda A G, Vidya T., Shama, Jayaram M.A, Manjunath A.S. (2013) Improving performance of K-means clustering by initializing cluster centers using genetic algorithm and entropy based fuzzy clustering for categorization of diabetic patients. In: Proceedings of international conference on advances in computing, Springer India
Khan Shehroz S, Ahmad Amir (2004) Cluster center initialization algorithm for K-means clustering. Elsevier, Journal pattern Recognition Letters
Singh, R.V., Bhatia, M.P.S. (2011) Data clustering with modified K-means algorithm. In: International conference on recent trends in information technology (ICRTIT), IEEE
Taoying Li, Chen Y (2008) An improved k-means algorithm for clustering using entropy weighting measures. In: 7th world congress on intelligent control and automation (WCICA), IEEE
Usman G, Ahmad U, Ahmad M (2013) Improved K-means clustering algorithm by getting initial cenroids. World Appl Sci J 27:543
Wang J, Su X. (2011) An improved K-means clustering algorithm. In: 3rd international conference on communication software and networks (ICCSN), IEEE
Xie J, Jiang S. (2010) A simple and fast algorithm for global K-means clustering. In: Second international workshop on education technology and computer science (ETCS), IEEE
Zhu J, Wang H (2010) An improved K-means clustering algorithm, The 2nd international conference on information management and engineering (ICIME), IEEE
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kant, S., Ansari, I.A. An improved K means clustering with Atkinson index to classify liver patient dataset. Int J Syst Assur Eng Manag 7 (Suppl 1), 222–228 (2016). https://doi.org/10.1007/s13198-015-0365-3
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-015-0365-3