Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Giuseppe Boari; Marta Nai Ruscone

Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Giuseppe Boari, Marta Nai Ruscone

Università Cattolica del Sacro Cuore

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

Original language	English
Title of host publication	Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper
Pages	1-4
Number of pages	4
Publication status	Published - 2012
Event	JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group - Anacapri Duration: 3 Sept 2012 → 4 Sept 2012

Conference

Conference	JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group
City	Anacapri
Period	3/9/12 → 4/9/12

Keywords

cluster analysis
intraclass correlation coefficient

Cite this

@inproceedings{5766ee6cabb349b593e4662fbe91f091,

title = "Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption",

abstract = "The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheff{\'e}, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.",

keywords = "cluster analysis, intraclass correlation coefficient, cluster analysis, intraclass correlation coefficient",

author = "Giuseppe Boari and {Nai Ruscone}, Marta",

year = "2012",

language = "English",

isbn = "978-88-6129-916-0",

pages = "1--4",

booktitle = "Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper",

note = "JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group ; Conference date: 03-09-2012 Through 04-09-2012",

}

TY - GEN

T1 - Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

AU - Boari, Giuseppe

AU - Nai Ruscone, Marta

PY - 2012

Y1 - 2012

N2 - The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

AB - The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). A definition of the intraclass correlation coefficient is given on the basis of a normal random effect model. The theory concerning r is well established for single variables analysis (Sheffé, 1959; Rao, 1973). We propose to consider a multiple test procedure in order to define the optimal clustering solution under normality assumption of the involved variables, using the test of null intraclass correlation.

KW - cluster analysis

KW - intraclass correlation coefficient

KW - cluster analysis

KW - intraclass correlation coefficient

UR - http://hdl.handle.net/10807/31690

M3 - Conference contribution

SN - 978-88-6129-916-0

SP - 1

EP - 4

BT - Analysis and Modeling of Complex Data in Behavioural and Social Sciences. Book of Short Paper

T2 - JCS-Cladag 2012. Joint Meeting of the Japanise Classification Society and the Italian Classification and Data Analyis Group

Y2 - 3 September 2012 through 4 September 2012

ER -

Use of ICC for Defining the Optimal Clustering Solution under Normality Assumption

Abstract

Conference

Keywords

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