An Algebraic Approach to Clustering and Classification with Support Vector Machines

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 d...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-01, Vol.10 (1), p.128
Main Authors: Arslan, Güvenç, Madran, Uğur, Soyoğlu, Duygu
Format: Article
Language:English
Subjects:
algebraic statistics
Algorithms
Classification
clique
Clustering
Data analysis
Datasets
Machine learning
Mathematical analysis
Optimization
Similarity measures
support vector machine
Support vector machines
Training
Vectors (mathematics)
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10010128