Clustering Based on Classification Quality
| Main Authors: | Tri Riyadi Yanto, Iwan, Rohmat Saedudin, Rd, Hartama, Dedy, Herawan, Tutut |
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| Format: | Article PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
https://link.springer.com/chapter/10.1007/978-3-319-51281-5_33
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| Subjects: | |
| Online Access: |
http://eprints.uad.ac.id/11645/1/Clustering%20Based%20on%20Classification%20Quality.pdf http://eprints.uad.ac.id/11645/ |
Daftar Isi:
- Clustering a set of objects into homogeneous classes is a fundamental operation in data mining. Categorical data clustering based on rough set theory has been an active research area in the field of machine learning. However, pure rough set theory is not well suited for analyzing noisy information systems. In this paper, an alternative technique for categorical data clustering using Variable Precision Rough Set model is proposed. It is based on the classification quality of Variable Precision Rough theory. The technique is implemented in MATLAB. Experimental results on three benchmark UCI datasets indicate that the technique can be successfully used to analyze grouped categorical data because it produces better clustering results. Keywords : Clustering; Rough set; Variable precision rough set model, classification quality