TL;DR
This paper proposes COSOHUC (Column Sorting Heuristic for Clustering), a deterministic heuristic initialization method that independently sorts numerical features, partitions the sorted values, computes partition means, and combines corresponding means to construct synthetic initial centers.
Sector-Mean Initialization is proposed, a deterministic initialization strategy with O(N) time complexity that partitions the two-dimensional data space into angular sectors around the global centroid and initializes centroids using sector-wise means.
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...
A comparative analysis of optimization techniques for the minimum sum-of-squares clustering (MSSC) problem in the context of big data reveals that no method is universally preferable and that greater algorithmic complexity does not by itself ensure a better practical trade-off.
A structured overview of classical and advanced clustering approaches, including hierarchical, partition-based, density-based, density-based, model-based, subspace, grid-based, and search-based metaheuristic techniques are provided.
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.
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...
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