Abstract
Real-world data generally deals with inconsistency. The uncertain k-means (UK-means) clustering algorithm, a modification of k-means, handles uncertain objects whose positions are represented by probability density functions (pdfs). Various techniques have been developed to enhance the performance of UK-means clustering algorithm but they are all centered on two major factors: choosing initial cluster centers and determining the number of clusters. This paper proposes that the measure of “closeness” is also a critical factor in deciding the quality of clusters. In this paper, the authors study the performance of UK-means clustering algorithm on four different distance functions using Haberman’s survival dataset. The analysis is performed on the basis of Davies–Bouldin index and purity values.
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Aggarwal, S., Agarwal, N., Jain, M. (2019). Performance Analysis of Uncertain K-means Clustering Algorithm Using Different Distance Metrics. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_19
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DOI: https://doi.org/10.1007/978-981-13-1132-1_19
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