Improved normalization and standardization techniques for higher purity in K-means clustering
Clustering is basically one of the major sources of primary data mining tools, which make researchers understand the natural grouping of attributes in datasets. Clustering is an unsupervised classification method with aim of partitioning, where objects in the same cluster are similar, and objects be...
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| Format: | Article |
| Language: | English |
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Pushpa Publishing House
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/54519/ http://psasir.upm.edu.my/id/eprint/54519/1/Improved%20normalization%20and%20standardization%20techniques%20for%20higher%20purity%20in%20K-means%20clustering.pdf |
| _version_ | 1848852567651516416 |
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| author | Dalatu, Paul Inuwa Fitrianto, Anwar Mustapha, Aida |
| author_facet | Dalatu, Paul Inuwa Fitrianto, Anwar Mustapha, Aida |
| author_sort | Dalatu, Paul Inuwa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Clustering is basically one of the major sources of primary data mining tools, which make researchers understand the natural grouping of attributes in datasets. Clustering is an unsupervised classification method with aim of partitioning, where objects in the same cluster are similar, and objects belong to different clusters vary significantly, with respect to their attributes. The K-means algorithm is a famous and fast technique in non-hierarchical cluster algorithms. Based on its simplicity, the K-means algorithm has been used in many fields. This paper proposes improved normalization and standardization techniques for higher purity in K-means clustering experimented with benchmark datasets from UCI machine learning repository and it was found that all the proposed techniques’ performance was much higher compared to the conventional K-means and the three classic transformations, and it is evidently shown by purity and Rand index accuracy results. |
| first_indexed | 2025-11-15T10:40:08Z |
| format | Article |
| id | upm-54519 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:40:08Z |
| publishDate | 2016 |
| publisher | Pushpa Publishing House |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-545192018-03-27T01:36:34Z http://psasir.upm.edu.my/id/eprint/54519/ Improved normalization and standardization techniques for higher purity in K-means clustering Dalatu, Paul Inuwa Fitrianto, Anwar Mustapha, Aida Clustering is basically one of the major sources of primary data mining tools, which make researchers understand the natural grouping of attributes in datasets. Clustering is an unsupervised classification method with aim of partitioning, where objects in the same cluster are similar, and objects belong to different clusters vary significantly, with respect to their attributes. The K-means algorithm is a famous and fast technique in non-hierarchical cluster algorithms. Based on its simplicity, the K-means algorithm has been used in many fields. This paper proposes improved normalization and standardization techniques for higher purity in K-means clustering experimented with benchmark datasets from UCI machine learning repository and it was found that all the proposed techniques’ performance was much higher compared to the conventional K-means and the three classic transformations, and it is evidently shown by purity and Rand index accuracy results. Pushpa Publishing House 2016-09 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54519/1/Improved%20normalization%20and%20standardization%20techniques%20for%20higher%20purity%20in%20K-means%20clustering.pdf Dalatu, Paul Inuwa and Fitrianto, Anwar and Mustapha, Aida (2016) Improved normalization and standardization techniques for higher purity in K-means clustering. Far East Journal of Mathematical Sciences, 100 (6). pp. 859-871. ISSN 0972-0871 http://www.pphmj.com/abstract/10134.htm Normalization; Standardization; K-means algorithm; Clustering; Purity; Rand index 10.17654/MS100060859 |
| spellingShingle | Normalization; Standardization; K-means algorithm; Clustering; Purity; Rand index Dalatu, Paul Inuwa Fitrianto, Anwar Mustapha, Aida Improved normalization and standardization techniques for higher purity in K-means clustering |
| title | Improved normalization and standardization techniques for higher purity in K-means clustering |
| title_full | Improved normalization and standardization techniques for higher purity in K-means clustering |
| title_fullStr | Improved normalization and standardization techniques for higher purity in K-means clustering |
| title_full_unstemmed | Improved normalization and standardization techniques for higher purity in K-means clustering |
| title_short | Improved normalization and standardization techniques for higher purity in K-means clustering |
| title_sort | improved normalization and standardization techniques for higher purity in k-means clustering |
| topic | Normalization; Standardization; K-means algorithm; Clustering; Purity; Rand index |
| url | http://psasir.upm.edu.my/id/eprint/54519/ http://psasir.upm.edu.my/id/eprint/54519/ http://psasir.upm.edu.my/id/eprint/54519/ http://psasir.upm.edu.my/id/eprint/54519/1/Improved%20normalization%20and%20standardization%20techniques%20for%20higher%20purity%20in%20K-means%20clustering.pdf |