A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients

Md Atiqul Islam; Behrooz Z. Alizadeh; Edwin R. van den Heuvel; Richard Bruggeman; Wiepke Cahn; Lieuwe de Haan; René S. Kahn; Carin Meijer; Inez Myin-Germeys; Jim van Os; Durk Wiersma

doi:10.1080/23737484.2015.1103670

A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients

Md Atiqul Islam (Corresponding author)
, Behrooz Z. Alizadeh
, Edwin R. van den Heuvel
, Richard Bruggeman
, Wiepke Cahn
, Lieuwe de Haan
, René S. Kahn
, Carin Meijer
, Inez Myin-Germeys
, Jim van Os
, Durk Wiersma

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

15 Citaten (Scopus)

Samenvatting

Finding clusters in a complex dataset is not straightforward. Different indices were developed to quantify the number of clusters. Their performances were studied using unrealistic simulations, since they were considered at low dimensions. We investigated 14 indices for eight-dimensional data using simulations based on cognition measures. We focused on hierarchical clustering with Ward’s agglomerative technique. Results indicated that Duda and Hart, Hartigan and Gap/pc were best performing. They estimated the number of clusters within ±1 with high probabilities. Duda and Hart index was most consistent, while Gap/pc and WGap/pc together made a good distinction between single and multiple clusters.

Originele taal-2	Engels
Pagina's (van-tot)	98-113
Aantal pagina's	16
Tijdschrift	Communications in Statistics
Volume	1
Nummer van het tijdschrift	2
DOI's	https://doi.org/10.1080/23737484.2015.1103670
Status	Gepubliceerd - 3 apr. 2015

Financiering

The infrastructure for the GROUP study is funded through the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGZ Eindhoven en de kempen, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan Zorggroep, Prins Clauscentrum Sittard, RIAGG Roermond, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center (The Hague). Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal, Riagg Amersfoort and Delta).

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10.1080/23737484.2015.1103670

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Islam, M. A., Alizadeh, B. Z., van den Heuvel, E. R., Bruggeman, R., Cahn, W., de Haan, L., Kahn, R. S., Meijer, C., Myin-Germeys, I., van Os, J., & Wiersma, D. (2015). A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients. Communications in Statistics, 1(2), 98-113. https://doi.org/10.1080/23737484.2015.1103670

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title = "A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients",

abstract = "Finding clusters in a complex dataset is not straightforward. Different indices were developed to quantify the number of clusters. Their performances were studied using unrealistic simulations, since they were considered at low dimensions. We investigated 14 indices for eight-dimensional data using simulations based on cognition measures. We focused on hierarchical clustering with Ward{\textquoteright}s agglomerative technique. Results indicated that Duda and Hart, Hartigan and Gap/pc were best performing. They estimated the number of clusters within ±1 with high probabilities. Duda and Hart index was most consistent, while Gap/pc and WGap/pc together made a good distinction between single and multiple clusters.",

keywords = "Cluster analysis, cluster indices, hierarchical clustering, homogeneous subgroups, number of clusters",

author = "Islam, \{Md Atiqul\} and Alizadeh, \{Behrooz Z.\} and \{van den Heuvel\}, \{Edwin R.\} and Richard Bruggeman and Wiepke Cahn and \{de Haan\}, Lieuwe and Kahn, \{Ren{\'e} S.\} and Carin Meijer and Inez Myin-Germeys and \{van Os\}, Jim and Durk Wiersma",

year = "2015",

month = apr,

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doi = "10.1080/23737484.2015.1103670",

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Islam, MA, Alizadeh, BZ, van den Heuvel, ER, Bruggeman, R, Cahn, W, de Haan, L, Kahn, RS, Meijer, C, Myin-Germeys, I, van Os, J & Wiersma, D 2015, 'A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients', Communications in Statistics, vol. 1, nr. 2, blz. 98-113. https://doi.org/10.1080/23737484.2015.1103670

A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients. / Islam, Md Atiqul (Corresponding author); Alizadeh, Behrooz Z.; van den Heuvel, Edwin R. et al.
In: Communications in Statistics, Vol. 1, Nr. 2, 03.04.2015, blz. 98-113.

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

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T1 - A comparison of indices for identifying the number of clusters in hierarchical clustering

T2 - A study on cognition in schizophrenia patients

AU - Islam, Md Atiqul

AU - Alizadeh, Behrooz Z.

AU - van den Heuvel, Edwin R.

AU - Bruggeman, Richard

AU - Cahn, Wiepke

AU - de Haan, Lieuwe

AU - Kahn, René S.

AU - Meijer, Carin

AU - Myin-Germeys, Inez

AU - van Os, Jim

AU - Wiersma, Durk

PY - 2015/4/3

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N2 - Finding clusters in a complex dataset is not straightforward. Different indices were developed to quantify the number of clusters. Their performances were studied using unrealistic simulations, since they were considered at low dimensions. We investigated 14 indices for eight-dimensional data using simulations based on cognition measures. We focused on hierarchical clustering with Ward’s agglomerative technique. Results indicated that Duda and Hart, Hartigan and Gap/pc were best performing. They estimated the number of clusters within ±1 with high probabilities. Duda and Hart index was most consistent, while Gap/pc and WGap/pc together made a good distinction between single and multiple clusters.

AB - Finding clusters in a complex dataset is not straightforward. Different indices were developed to quantify the number of clusters. Their performances were studied using unrealistic simulations, since they were considered at low dimensions. We investigated 14 indices for eight-dimensional data using simulations based on cognition measures. We focused on hierarchical clustering with Ward’s agglomerative technique. Results indicated that Duda and Hart, Hartigan and Gap/pc were best performing. They estimated the number of clusters within ±1 with high probabilities. Duda and Hart index was most consistent, while Gap/pc and WGap/pc together made a good distinction between single and multiple clusters.

KW - Cluster analysis

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KW - hierarchical clustering

KW - homogeneous subgroups

KW - number of clusters

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A comparison of indices for identifying the number of clusters in hierarchical clustering: A study on cognition in schizophrenia patients

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