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Exploratory tools for clustering multivariate data
- Source :
- Computational Statistics & Data Analysis. 52:272-285
- Publication Year :
- 2007
- Publisher :
- Elsevier BV, 2007.
-
Abstract
- The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible polynomial approximations to the envelopes are given. New graphical tools provide methods not only of detecting clusters but also of determining their membership. Comparisons are made with mclust and k-means clustering.
- Subjects :
- Statistics and Probability
Mahalanobis distance
Multivariate statistics
Polynomial
Applied Mathematics
k-means clustering
Multivariate normal distribution
computer.software_genre
Computational Mathematics
Computational Theory and Mathematics
Test statistic
Data mining
Cluster analysis
Algorithm
computer
Mathematics
Statistical hypothesis testing
Subjects
Details
- ISSN :
- 01679473
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- Computational Statistics & Data Analysis
- Accession number :
- edsair.doi...........657c32f102c839e19875d9df6fe609e4
- Full Text :
- https://doi.org/10.1016/j.csda.2006.12.034