Cluster detection and clustering with random start forward searches

Anthony C. Atkinson, Marco Riani and Andrea Cerioli

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The forward search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the forward search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the forward search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with 8 clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.

Keywords: brushing; data structure; forward search; graphical methods; linked plots; Mahalanobis distance; MM estimation; outliers; S estimation; Tukey’s biweight. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2017-04-08
New Economics Papers: this item is included in nep-cmp and nep-ecm
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Citations: View citations in EconPapers (2)

Published in Journal of Applied Statistics, 8, April, 2017, 45(5), pp. 777-798. ISSN: 0266-4763

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:72291

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