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YAC2: An α-proximity based clustering algorithm
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DOI：10.1016/j.eswa.2020.114138.png)
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Abstract
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Clustering is the process of identifying objects with similar intrinsic properties and grouping them together into separate clusters. It also serves as a preliminary step in data classification by separating heterogeneous data into reasonably homogeneous groups, which can be further processed. Existing literature discusses a host of different clustering algorithms and their applications, albeit no single approach for all applications has yet emerged. In this paper, we introduce a novel clustering algorithm, YAC2, based on data binning and alpha-proximity/neighborhood. The binning process is a data transformation step that converts cardinal values of the attributes into their ordinal equivalence. The algorithm introduces a specialized centroid that is used with the alpha-proximity and a matching algorithm to partition data set into a sequentially generated clusters. We present the results of applying YAC2 to a set of established benchmark data sets, using a host of evaluation metrics. Our results show YAC2 to perform well besting a number of well-established algorithms. For several metrics, YAC2 has provided improvements averagely in the range of 6% (in the Iris data set)-180% (in the Wholesale Customer data set) over other algorithms.
Keywords:
Clustering analysis
YAC2
Machine learning
Unsupervised algorithms
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