An Efficient Algorithm for Initializing Centroids in K-means Clustering
Clustering represents one of the most popular knowledge extraction algorithms in data mining techniques. Hierarchical and partitioning approaches are widely used in this field. Each has its own advantages, drawbacks and goals. K-means represents the most popular partitioning clusteringtechnique, how...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Computer Science and Mathematics, University of Kufa
2016-12-01
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| Series: | Journal of Kufa for Mathematics and Computer |
| Subjects: | |
| Online Access: | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2118 |
| _version_ | 1797267013640388608 |
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| author | Dr. Ahmed Hussain Aliwy Dr. Kadhim B. S. Aljanabi |
| author_facet | Dr. Ahmed Hussain Aliwy Dr. Kadhim B. S. Aljanabi |
| author_sort | Dr. Ahmed Hussain Aliwy |
| collection | DOAJ |
| description | Clustering represents one of the most popular knowledge extraction algorithms in data mining techniques. Hierarchical and partitioning approaches are widely used in this field. Each has its own advantages, drawbacks and goals. K-means represents the most popular partitioning clusteringtechnique, however it suffers from two major drawbacks; time complexity and its sensitivity to the initial centroid values. The work in this paper presents an approach for estimating the starting initial centroids throughout three process including density based, normalization and smoothing ideas. The proposed algorithm has a strong mathematical foundation.
The proposed approach was tested using a free standard data (20000 records). The results showed that the approach has better complexity and ensures the clustering convergence. |
| first_indexed | 2024-04-25T01:09:50Z |
| format | Article |
| id | doaj.art-dfa1a8f31a5d438ea18f2c15ea9f1949 |
| institution | Directory Open Access Journal |
| issn | 2076-1171 2518-0010 |
| language | English |
| last_indexed | 2024-04-25T01:09:50Z |
| publishDate | 2016-12-01 |
| publisher | Faculty of Computer Science and Mathematics, University of Kufa |
| record_format | Article |
| series | Journal of Kufa for Mathematics and Computer |
| spelling | doaj.art-dfa1a8f31a5d438ea18f2c15ea9f19492024-03-10T10:37:32ZengFaculty of Computer Science and Mathematics, University of KufaJournal of Kufa for Mathematics and Computer2076-11712518-00102016-12-013210.31642/JoKMC/2018/030203An Efficient Algorithm for Initializing Centroids in K-means ClusteringDr. Ahmed Hussain Aliwy0Dr. Kadhim B. S. Aljanabi1University of KufaUniversity of KufaClustering represents one of the most popular knowledge extraction algorithms in data mining techniques. Hierarchical and partitioning approaches are widely used in this field. Each has its own advantages, drawbacks and goals. K-means represents the most popular partitioning clusteringtechnique, however it suffers from two major drawbacks; time complexity and its sensitivity to the initial centroid values. The work in this paper presents an approach for estimating the starting initial centroids throughout three process including density based, normalization and smoothing ideas. The proposed algorithm has a strong mathematical foundation. The proposed approach was tested using a free standard data (20000 records). The results showed that the approach has better complexity and ensures the clustering convergence.https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2118Data MiningClusteringK-meansCentroids Complexity |
| spellingShingle | Dr. Ahmed Hussain Aliwy Dr. Kadhim B. S. Aljanabi An Efficient Algorithm for Initializing Centroids in K-means Clustering Journal of Kufa for Mathematics and Computer Data Mining Clustering K-means Centroids Complexity |
| title | An Efficient Algorithm for Initializing Centroids in K-means Clustering |
| title_full | An Efficient Algorithm for Initializing Centroids in K-means Clustering |
| title_fullStr | An Efficient Algorithm for Initializing Centroids in K-means Clustering |
| title_full_unstemmed | An Efficient Algorithm for Initializing Centroids in K-means Clustering |
| title_short | An Efficient Algorithm for Initializing Centroids in K-means Clustering |
| title_sort | efficient algorithm for initializing centroids in k means clustering |
| topic | Data Mining Clustering K-means Centroids Complexity |
| url | https://journal.uokufa.edu.iq/index.php/jkmc/article/view/2118 |
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