Abstract
Clustering algorithms for multidimensional numerical data must overcome special difficulties due to the irregularities of data distribution. We present a clustering algorithm for numerical data that combines ideas from random projection techniques and density-based clustering. The algorithm consists of two phases: the first phase that entails the use of random projections to detect clusters, and the second phase that consists of certain post-processing techniques of clusters obtained by several random projections. Experiments were performed on synthetic data consisting of randomly-generated points in ℝn, synthetic images containing colored regions randomly distributed, and, finally, real images. Our results suggest the potential of our algorithm for image segmentation.
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Urruty, T., Djeraba, C., Simovici, D.A. (2007). Clustering by Random Projections. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_9
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DOI: https://doi.org/10.1007/978-3-540-73435-2_9
Publisher Name: Springer, Berlin, Heidelberg
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