Abstract
This paper proposes an unsupervised clustering technique for data classification based on the K-means algorithm. The K-means algorithm is well known for its simplicity and low time complexity. However, the algorithm has three main drawbacks: dependency on the initial centroids, dependency on the number of clusters, and degeneracy. Our solution accommodates these three issues, by proposing an approach to automatically detect a semi-optimal number of clusters according to the statistical nature of the data. As a side effect, the method also makes choices of the initial centroid-seeds not critical to the clustering results. The experimental results show the robustness of the Y-means algorithm as well as its good performance against a set of other well known unsupervised clustering techniques. Furthermore, we study the performance of our proposed solution against different distance and outlier-detection functions and recommend the best combinations.
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Ghorbani, A.A., Onut, IV. (2010). Y-Means: An Autonomous Clustering Algorithm. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_1
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DOI: https://doi.org/10.1007/978-3-642-13769-3_1
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