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Clustering Based on Classification Quality

Tri Riyadi Yanto, Iwan and Rohmat Saedudin, Rd and Hartama, Dedy and Herawan, Tutut Clustering Based on Classification Quality. [Artikel Dosen]

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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

Item Type: Artikel Dosen
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisi / Prodi: Faculty of Mathematics and Natural Sciences (Fakultas Matematika dan Ilmu Pengetahuan Alam) > S1-Information System (S1-Sistem Informasi)
Depositing User: Iwan Tri Riyadi Yanto
Date Deposited: 06 Nov 2018 06:57
Last Modified: 06 Nov 2018 06:57

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