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Partition of Interval-Valued Observations Using Regression

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Abstract

Both regression modeling and clustering methodologies have been extensively studied as separate techniques. There has been some activity in using regression-based algorithms to partition a data set into clusters for classical data; we propose one such algorithm to cluster interval-valued data. The new algorithm is based on the k-means algorithm of MacQueen (1967) and the dynamical partitioning method of Diday and Simon (1976), with the partitioning criteria being based on establishing regression models for each sub-cluster. This also depends on distance measures between the underlying regression models for each sub-cluster. Several types of simulated data sets are generated for several different data structures. The proposed k-regressions algorithm consistently out-performs the k-means algorithm. Elbow plots are used to identify the total number of clusters K in the partition. The new method is also applied to real data.

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Authors and Affiliations

  1. Bank of America, Charlotte, NC, 28202, USA

    Fei Liu

  2. Department of Statistics, University of Georgia,, Athens, GA, 30602, USA

    L. Billard

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  1. Fei Liu
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Liu, F., Billard, L. Partition of Interval-Valued Observations Using Regression. J Classif 39, 55–77 (2022). https://doi.org/10.1007/s00357-021-09394-5

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