Speaker

Joshua Tobin from School of Computer Science & Statistics, Trinity College Dublin

Abstract

Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical clusters, and require a priori the unknown number of clusters. We develop a new clustering algorithm for large data of mixed type, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to mixed data; (2) the algorithm is capable of detecting outliers and clusters of relatively lower density values; (3) the algorithm is competent at deciding the correct number of clusters. The computational complexity of the algorithm is greatly reduced by applying a fast k-nearest neighbours method and by scaling down to component sets. We present experimental results to verify that our algorithm works well in practice.

Supplementary Materials

Paper: The paper is available on Arxiv here

Codes: The associated Python library, CPFcluster, is available here.

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Webinar Video

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