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

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

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...

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Bibliographic Details
Main Authors: Raheem, Ajiboye Adeleke, Hauwau, Isah-Kebbe, O., Oladele Tinuke
Format: Article
Language:English
Published: Foundation of Computer Science (FCS) 2014
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/1/Cluster_Analysis_of_Data_Points_using_Partitioning_and_Probabilistic_Model-based_Algorithms.pdf
id ump-6418
recordtype eprints
spelling ump-64182015-03-03T09:30:10Z http://umpir.ump.edu.my/id/eprint/6418/ Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms Raheem, Ajiboye Adeleke Hauwau, Isah-Kebbe O., Oladele Tinuke QA76 Computer software 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. Foundation of Computer Science (FCS) 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6418/1/Cluster_Analysis_of_Data_Points_using_Partitioning_and_Probabilistic_Model-based_Algorithms.pdf 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 http://dx.doi.org/10.5120/ijais14-451211 DOI: 10.5120/ijais14-451211
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Raheem, Ajiboye Adeleke
Hauwau, Isah-Kebbe
O., Oladele Tinuke
Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
description 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.
format Article
author Raheem, Ajiboye Adeleke
Hauwau, Isah-Kebbe
O., Oladele Tinuke
author_facet Raheem, Ajiboye Adeleke
Hauwau, Isah-Kebbe
O., Oladele Tinuke
author_sort Raheem, Ajiboye Adeleke
title Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
title_short Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
title_full Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
title_fullStr Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
title_full_unstemmed Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
title_sort cluster analysis of data points using partitioning and probabilistic model-based algorithms
publisher Foundation of Computer Science (FCS)
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/
http://umpir.ump.edu.my/id/eprint/6418/1/Cluster_Analysis_of_Data_Points_using_Partitioning_and_Probabilistic_Model-based_Algorithms.pdf
first_indexed 2023-09-18T22:02:09Z
last_indexed 2023-09-18T22:02:09Z
_version_ 1777414469552439296

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