Distance Metrics and Clustering Methods for Mixed-type Data
Abstract
In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed–type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Here, the guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.
- Authors:
-
[1];
- Univ. at Buffalo, Buffalo, NY (United States)
- Arenadotio, New York, NY (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1459931
- Report Number(s):
- SAND-2018-7091J
Journal ID: ISSN 0306-7734; 665360
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- International Statistical Review
- Additional Journal Information:
- Journal Volume: 87; Journal Issue: 1; Journal ID: ISSN 0306-7734
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; discretisation; dummy coding; Gower's distance; k-mean clustering; machine learning; Mahalanobis distance; mixture model; multivariate data analysis; unsupervised learning
Citation Formats
Foss, Alexander H., Markatou, Marianthi, and Ray, Bonnie. Distance Metrics and Clustering Methods for Mixed-type Data. United States: N. p., 2018.
Web. doi:10.1111/insr.12274.
Foss, Alexander H., Markatou, Marianthi, & Ray, Bonnie. Distance Metrics and Clustering Methods for Mixed-type Data. United States. https://doi.org/10.1111/insr.12274
Foss, Alexander H., Markatou, Marianthi, and Ray, Bonnie. Thu .
"Distance Metrics and Clustering Methods for Mixed-type Data". United States. https://doi.org/10.1111/insr.12274. https://www.osti.gov/servlets/purl/1459931.
@article{osti_1459931,
title = {Distance Metrics and Clustering Methods for Mixed-type Data},
author = {Foss, Alexander H. and Markatou, Marianthi and Ray, Bonnie},
abstractNote = {In spite of the abundance of clustering techniques and algorithms, clustering mixed interval (continuous) and categorical (nominal and/or ordinal) scale data remain a challenging problem. In order to identify the most effective approaches for clustering mixed–type data, we use both theoretical and empirical analyses to present a critical review of the strengths and weaknesses of the methods identified in the literature. Here, the guidelines on approaches to use under different scenarios are provided, along with potential directions for future research.},
doi = {10.1111/insr.12274},
journal = {International Statistical Review},
number = 1,
volume = 87,
place = {United States},
year = {Thu Jun 21 00:00:00 EDT 2018},
month = {Thu Jun 21 00:00:00 EDT 2018}
}
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Cited by: 24 works
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Figure 1: Simulation results: performance of k-modes clustering and latent class analysis (LCA) for various quantile splits of the data. A mixed-type data set was generated with two underlying clusters, and the interval scale variable was discretised using a quantile split with the number of bins shown along the x-axis.more »
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Works referencing / citing this record:
Distance‐based clustering of mixed data
journal, November 2018
- van de Velden, Michel; Iodice D'Enza, Alfonso; Markos, Angelos
- Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 11, Issue 3
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