Abstract
Clustering is a powerful exploratory technique for extracting the knowledge of given data. Several clustering techniques that have been proposed require predetermined number of clusters. However, the triangular kernel-nearest neighbor-based clustering (TKNN) has been proven able to determine the number and member of clusters automatically. TKNN provides good solutions for clustering non-spherical and high-dimensional data without prior knowledge of data labels. On the other hand, there is no definite measure to evaluate the accuracy of the clustering result. In order to evaluate the performance of the proposed TKNN clustering algorithm, we utilized various benchmark classification datasets. Thus, TKNN is proposed for discovering true clusters with arbitrary shape, size and density contained in the datasets. The experimental results on benched-mark datasets showed the effectiveness of our technique. Our proposed TKNN achieved more accurate clustering results and required less time processing compared with k-means, ILGC, DBSCAN and KFCM.
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Musdholifah, A., Hashim, S.Z.M. (2013). Triangular Kernel Nearest-Neighbor-Based Clustering Algorithm for Discovering True Clusters. In: Washio, T., Luo, J. (eds) Emerging Trends in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36778-6_11
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DOI: https://doi.org/10.1007/978-3-642-36778-6_11
Publisher Name: Springer, Berlin, Heidelberg
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