Determination of the Number of Clusters in a Data Set

Autor:	Derrick S. Boone
Rok vydání:	2011
Předmět:	Determining the number of clusters in a data set CURE data clustering algorithm Correlation clustering Single-linkage clustering Constrained clustering Affinity propagation Data mining Cluster analysis computer.software_genre Algorithm computer k-medians clustering Mathematics
Zdroj:	International Journal of Strategic Decision Sciences. 2:1-13
ISSN:	1947-8577 1947-8569
Popis:	The accuracy of “stopping rules” for determining the number of clusters in a data set is examined as a function of the underlying clustering algorithm being used. Using a Monte Carlo study, various stopping rules, used in conjunction with six clustering algorithms, are compared to determine which rule/algorithm combinations best recover the true number of clusters. The rules and algorithms are tested using disparately sized, artificially generated data sets that contained multiple numbers and levels of clusters, variables, noise, outliers, and elongated and unequally sized clusters. The results indicate that stopping rule accuracy depends on the underlying clustering algorithm being used. The cubic clustering criterion (CCC), when used in conjunction with mixture models or Ward’s method, recovers the true number of clusters more accurately than other rules and algorithms. However, the CCC was more likely than other stopping rules to report more clusters than are actually present. Implications are discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::475690cca51a5fa5e209d49c3e425294 https://doi.org/10.4018/jsds.2011100101 Zobrazit plný text záznamu