Penalized cluster analysis with applications to family data

Yixin Fang; Junhui Wang

doi:10.1016/j.csda.2011.01.004

Penalized cluster analysis with applications to family data

Yixin Fang
, Junhui Wang

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The goal of cluster analysis is to assign observations into clusters so that observations in the same cluster are similar in some sense. Many clustering methods have been developed in the statistical literature, but these methods are inappropriate for clustering family data, which possess intrinsic familial structure. To incorporate the familial structure, we propose a form of penalized cluster analysis with a tuning parameter controlling the tradeoff between the observation dissimilarity and the familial structure. The tuning parameter is selected based on the concept of clustering stability. The effectiveness of the method is illustrated via simulations and an application to a family study of asthma.

Original language	American English
Pages (from-to)	2128-2136
Number of pages	9
Journal	Computational Statistics and Data Analysis
Volume	55
Issue number	6
DOIs	https://doi.org/10.1016/j.csda.2011.01.004
State	Published - Jun 1 2011
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

Keywords

Consistency
Cross-validation
K-means
Kinship
Stability

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10.1016/j.csda.2011.01.004

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title = "Penalized cluster analysis with applications to family data",

abstract = "The goal of cluster analysis is to assign observations into clusters so that observations in the same cluster are similar in some sense. Many clustering methods have been developed in the statistical literature, but these methods are inappropriate for clustering family data, which possess intrinsic familial structure. To incorporate the familial structure, we propose a form of penalized cluster analysis with a tuning parameter controlling the tradeoff between the observation dissimilarity and the familial structure. The tuning parameter is selected based on the concept of clustering stability. The effectiveness of the method is illustrated via simulations and an application to a family study of asthma.",

keywords = "Consistency, Cross-validation, K-means, Kinship, Stability",

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T1 - Penalized cluster analysis with applications to family data

AU - Fang, Yixin

AU - Wang, Junhui

PY - 2011/6/1

Y1 - 2011/6/1

N2 - The goal of cluster analysis is to assign observations into clusters so that observations in the same cluster are similar in some sense. Many clustering methods have been developed in the statistical literature, but these methods are inappropriate for clustering family data, which possess intrinsic familial structure. To incorporate the familial structure, we propose a form of penalized cluster analysis with a tuning parameter controlling the tradeoff between the observation dissimilarity and the familial structure. The tuning parameter is selected based on the concept of clustering stability. The effectiveness of the method is illustrated via simulations and an application to a family study of asthma.

AB - The goal of cluster analysis is to assign observations into clusters so that observations in the same cluster are similar in some sense. Many clustering methods have been developed in the statistical literature, but these methods are inappropriate for clustering family data, which possess intrinsic familial structure. To incorporate the familial structure, we propose a form of penalized cluster analysis with a tuning parameter controlling the tradeoff between the observation dissimilarity and the familial structure. The tuning parameter is selected based on the concept of clustering stability. The effectiveness of the method is illustrated via simulations and an application to a family study of asthma.

KW - Consistency

KW - Cross-validation

KW - K-means

KW - Kinship

KW - Stability

UR - https://www.scopus.com/pages/publications/79952038641

UR - https://www.scopus.com/pages/publications/79952038641#tab=citedBy

U2 - 10.1016/j.csda.2011.01.004

DO - 10.1016/j.csda.2011.01.004

M3 - Article

SN - 0167-9473

VL - 55

SP - 2128

EP - 2136

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 6

ER -

Penalized cluster analysis with applications to family data

Abstract

ASJC Scopus subject areas

Keywords

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