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

Raheem, Ajiboye Adeleke and Hauwau, Isah-Kebbe and O., Oladele Tinuke (2014) Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms. International Journal of Applied Information Systems (IJAIS), 7 (7). pp. 21-26. ISSN 2249-0868. (Published)

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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.

Item Type: Article
Uncontrolled Keywords: Clustering; Algorithm; K-means; EM-clustering; K-medoids
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 08 Sep 2014 07:31
Last Modified: 03 Mar 2015 09:30
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