Distance Metrics and Clustering Methods for Mixed‐type Data-Reference-Cited by-同舟云学术

Distance Metrics and Clustering Methods for Mixed‐type Data

Published:2018-06-21 Issue:1 Volume:87 Page:80-109
ISSN:0306-7734
Container-title:International Statistical Review
language:en
Short-container-title:International Statistical Review

Author:

Foss Alexander H.¹^ORCID,Markatou Marianthi¹,Ray Bonnie²

Affiliation:

1. Department of BiostatisticsUniversity at Buffalo 706 Kimball Tower Buffalo 14214 NY USA

2. Arenadotio New York NY USA

Funder

Patient-Centered Outcomes Research Institute (PCORI)

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12274

Reference141 articles.

1. Afendras G.&Markatou M.(2015).Optimality of training/test size and resampling effectiveness of cross‐validation estimators of the generalization error. arXiv 1511.02980.

2. A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

3. Statistical Modelling of Data on Teaching Styles

4. A new look at the statistical model identification

5. Identifiability of parameters in latent structure models with many observed variables

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