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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| 格式: | 文件 |
| 语言: | English |
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Foundation of Computer Science (FCS)
2014
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| 主题: | |
| 在线阅读: | http://umpir.ump.edu.my/id/eprint/6418/1/Cluster_Analysis_of_Data_Points_using_Partitioning_and_Probabilistic_Model-based_Algorithms.pdf |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T11:46:34Z |
| format | Article |
| id | UMPir6418 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:46:34Z |
| publishDate | 2014 |
| publisher | Foundation of Computer Science (FCS) |
| record_format | dspace |
| spelling | UMPir64182015-03-03T09:30:10Z 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. (Published) http://dx.doi.org/10.5120/ijais14-451211 DOI: 10.5120/ijais14-451211 |
| 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 |
| title | 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_short | 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 |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/6418/1/Cluster_Analysis_of_Data_Points_using_Partitioning_and_Probabilistic_Model-based_Algorithms.pdf |
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