Mixture Model Tests Of Hierarchical Clustering Algorithms
The Problem Of Classifying Everybody
Bibliographic Data
| ID | 19290201 |
|---|---|
| Authors | Craig Edelbrock (National Institutes of Health, corresponding author) |
| Year | 1979 |
| Volume | 14 |
| Issue | 3 |
| Pages | 367-384 |
| Publication date | 1979-07-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1207/s15327906mbr1403_6 |
| PMID | 26821856 |
| OpenAlex | W2006982253 |
| Language | EN |
| Citations received | 39 |
Due to the effects of outliers, mixture model tests that require all objects to be classified can severely underestimate the accuracy of hierarchical clustering algorithms. More valid and relevant comparisons between algorithms can be made by calculating accuracy at several levels in the hierarchical tree and considering accuracy as a function of the coverage of the classification. Using this procedure, several algorithms were compared on their ability to resolve ten multivariate normal mixtures. All of the algorithms were significantly more accurate than a random linkage algorithm, and accuracy was inversely related to coverage. Algorithms using correlation as the similarity measure were significantly more accurate than those using Euclidean distance (p < .001). A subset of high accuracy algorithms, including single, average, and centroid linkage using correlation, and Ward's minimum variance technique, was identified
Algorithm · Centroid · Cluster analysis · Correlation · Data mining · Euclidean distance · Hierarchical clustering · Image (mathematics) · Linkage (software) · Multivariate statistics · Outlier · Pattern recognition (psychology) · Similarity (geometry) · Statistics · Variance (accounting) · Advanced Statistical Methods and Models · Artificial Intelligence · Bayesian Methods and Mixture Models · Computer Science · Mathematics · Statistical Methods and Applications
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| Unique citing works | 39 |
|---|---|
| Citations per year | 0,85 |
| Citation span | 1980 - 2025 (46) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 37 |