An Algebraic Approach to Clustering and Classification with Support Vector Machines

Güvenç Arslan, Uğur Madran and Duygu Soyoğlu
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Güvenç Arslan: Department of Statistics, Kırıkkale University, Kırıkkale 71450, Turkey
Uğur Madran: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Duygu Soyoğlu: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Mathematics, 2022, vol. 10, issue 1, 1-19

Abstract: In this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors.

Keywords: clique; algebraic statistics; machine learning; clustering; classification; support vector machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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