Distance Metrics and Clustering Methods for Mixed-type Data
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]
; [1]
; [2]
- Univ. at Buffalo, Buffalo, NY (United States)
- Arenadotio, New York, NY (United States)
- Publication Date:
- Report Number(s):
- SAND-2018-7091J
Journal ID: ISSN 0306-7734; 665360
- Grant/Contract Number:
- AC04-94AL85000
- Type:
- Accepted Manuscript
- Journal Name:
- International Statistical Review
- Additional Journal Information:
- Journal Name: International Statistical Review; Journal ID: ISSN 0306-7734
- Research Org:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org:
- USDOE National Nuclear Security Administration (NNSA)
- 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
- OSTI Identifier:
- 1459931