Clustering of Big Data with Mixed Features

• URL: http://arxiv.org/abs/2011.06043v1
• Date: Wed, 11 Nov 2020 19:54:38 GMT
• Title: Clustering of Big Data with Mixed Features
• Authors: Joshua Tobin, Mimi Zhang
• Abstract summary: We develop a new clustering algorithm for large data of mixed type. The algorithm is capable of detecting outliers and clusters of relatively lower density values. We present experimental results to verify that our algorithm works well in practice.
• Score: 3.3504365823045044
• License: http://creativecommons.org/licenses/by/4.0/
• 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 here 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 neighbors method and by scaling down to component sets. We present experimental results to verify that our algorithm works well in practice. Keywords: Clustering; Big Data; Mixed Attribute; Density Peaks; Nearest-Neighbor Graph; Conductance.

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