The paper proposes a modification of the standard k-means algorithm that removes low-probability tail points of Gaussian components using the p-quantile of the chi-squared distribution as a trimming threshold based on the squared Mahalanobis distance. The procedure is evaluated on synthetic noisy datasets and compared with well-known state-of-the-art clustering methods. Applications in circular image pattern recognition demonstrate effective detection of spherical patterns in highly noisy environments. Pattern detection is driven by an optimization using a constructed spherical clustering-validity index, and the method’s practical potential is confirmed by comparisons with state-of-the-art circular detection techniques.
A robust clustering method that estimates cluster centers and covariance matrices using density power divergence measures combined with Mahalanobis distance, making it resistant to outliers and adaptable to heterogeneous, elliptical clusters, unlike the classical K-means algorithm is introduced.
Anirban Mondal, Paromita Banerjee, A. Mandal· 0 citations
We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and Cauchy distributions-within a finite mixture modeling framework for robust model-based clustering and outlier detection. To ensure parsimony and coherence within clusters, shared location and scale parameters a...
Babak F. Dehkordi, Jeffrey L. Andrews, Andrew Jirasek· 0 citations
For image data, matrix-based clustering methods are gaining popularity because they can directly process two-dimensional (2D) data structures without vectorization. However, most existing approaches rely on squared Frobenius norms in their objective functions, making them sensitive to outliers and noise commonly encoun...
In unsupervised learning, Clustering is a core method used to determine unseen arrangements and structures within datasets by grouping similar instances together. Among the many clustering algorithms, Among clustering techniques, K-Means continues to be one of the most popular owing to its ease of implementation, fast...
I. Khan, H. Daud, Rajalingam Sokkalingam et al.· International Conference on...· 0 citations
Clustering high-dimensional data is challenging when irrelevant or weakly informative features obscure the underlying cluster structure. To address this issue, we propose two sparse k-means clustering algorithms, S-KM1 and S-KM2, that perform cluster estimation and feature selection by sparse feature weighting simultan...
This paper proposes the CERFW-FCM (Conservative Entropy-Regularized Feature-Weighted Fuzzy C-Means) method as a conservatively stabilized extension of the classical fuzzy C-means algorithm for fuzzy clustering of multidimensional numerical data with heterogeneous feature informativeness. The study is motivated by the o...
D. Symonov, Y. Symonov, Bohdan Zaika· International Scientific Tec...· 0 citations
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