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A general iterative clustering algorithm

Published:2022-01-31 Issue:4 Volume:15 Page:433-446
ISSN:1932-1864
Container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal
language:en
Short-container-title:Statistical Analysis

Author:

Lin Ziqiang¹^ORCID,Laska Eugene¹²³,Siegel Carole¹²

Affiliation:

1. Department of Psychiatry New York University Langone School of Medicine New York NY USA

2. Department of Population Health, Division of Biostatistics New York University Langone School of Medicine New York NY USA

3. One Park Avenue, New York NY 10016 USA

Funder

National Institute on Alcohol Abuse and Alcoholism

Publisher

Wiley

Subject

Computer Science Applications,Information Systems,Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11573

Reference43 articles.

1. Cluster ensemble based on Random Forests for genetic data

2. A comparative study of data-dependent approaches without learning in measuring similarities of data objects

3. K-Random Forests: a K-means style algorithm for Random Forest clustering

4. On learning Random Forests for Random Forest-clustering

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1. Hierarchical and two-step clustering to assess symptoms of sick building syndrome in office spaces in an educational building;Architectural Engineering and Design Management;2024-07-03

2. A treeless absolutely random forest with closed‐form estimators of expected proximities;Statistical Analysis and Data Mining: The ASA Data Science Journal;2024-04

3. Supporting weather forecasting performance management at aerodromes through anomaly detection and hierarchical clustering;Expert Systems with Applications;2023-03

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