Kernel probabilistic distance clustering algorithms

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2022-7

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Özkan, Dilay

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Clustering is an unsupervised learning method that groups data considering the similarities between objects (data points). Probabilistic Distance Clustering (PDC) is a soft clustering approach based on some principles. Instead of directly assigning an object to a cluster, it assigns them to clusters with a membership probability. PDC is a simple yet effective clustering algorithm that performs well on spherical-shaped and linearly separable data sets. Traditional clustering algorithms fail when the data set is non-spherical or non-linearly separable, as in the case of PDC. The kernel method overcomes this problem by implicitly mapping the data into a higher dimensional space via a nonlinear transformation, where the data may be linearly separable. This study focuses on developing kernel-based clustering algorithms using the principles of PDC to overcome the problem of clustering non-spherical or non-linearly separable data sets and proposes three kernel-based PDC algorithms. In addition, different than the classical approach, Mahalanobis distance is also considered in kernel clustering, and a new kernel-based Mahalanobis distance is developed to be used in soft kernel clustering techniques. An experimental study is conducted for real and synthetic data sets to measure the performance of the proposed kernel-based PDC algorithms.

Subject Keywords

Clustering, Probabilistic clustering, Kernel functions, Kernel method

URI

https://hdl.handle.net/11511/98190

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

D. Özkan, “Kernel probabilistic distance clustering algorithms,” M.S. - Master of Science, Middle East Technical University, 2022.