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Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Ajiboye Adeleke R. , Isah-kebbe Hauwau, Oladele Tinuke O. Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451211
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  1. Ajiboye Adeleke R., Isah-kebbe Hauwau and Oladele Tinuke O.. Article: Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms. International Journal of Applied Information Systems 7(7):21-26, August 2014. BibTeX

    @article{key:article,
    	author = "Ajiboye Adeleke R. and Isah-kebbe Hauwau and Oladele Tinuke O.",
    	title = "Article: Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 7,
    	pages = "21-26",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Exploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points.

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Keywords

Clustering, Algorithm, K-means, EM-clustering, K-medoids