Clustering with implicit constraints: A novel approach to housing market segmentation

Document Type

Article

Publication Date

4-1-2022

Abstract

Constrained clustering has been widely studied and outperforms both the traditional unsupervised clustering and experience-oriented approaches. However, the existing literature on constrained clustering concentrates on spatially explicit constraints, while many constraints in housing market studies are implicit. Ignoring the implicit constraints will result in unreliable clustering results. This article develops a novel framework for constrained clustering, which takes implicit constraints into account. Specifically, the research extends the classical greedy searching algorithm by adding one back-and-forth searching step, efficiently coping with the order sensitivity. Via evaluation on both synthetic and real data sets, it turns out that the proposed algorithm outperforms existing algorithms, even when only the traditional pairwise constraints are provided. In an application to a concrete housing market segmentation problem, the proposed algorithm shows its power to accommodate user-specified homogeneity criteria to extract hidden information on the underlying urban spatial structure.

Identifier

85121673790 (Scopus)

Publication Title

Transactions in GIS

External Full Text Location

https://doi.org/10.1111/tgis.12878

e-ISSN

14679671

ISSN

13611682

First Page

585

Last Page

608

Issue

2

Volume

26

Grant

20YJC790176

Fund Ref

Ministry of Education of the People's Republic of China

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