Paper Info
Title
Data Model and Classification by Trees: The Minimum Variance Reduction (MVR) Method
Abstract
O  (n 4), where n is the number of objects. We describe the application of the MVR method to two data models: the weighted least-squares (WLS) model (V is diagonal), where the MVR method can be reduced to an O(n 3) time complexity; a model arising from the study of biological sequences, which involves a complex non-diagonal V matrix that is estimated from the dissimilarity matrix Δ. For both models, we provide simulation results that show a significant error reduction in the reconstruction of T, relative to classical agglomerative algorithms.
Year
DOI
Venue
2000
10.1007/s003570000005
J. Classification
Keywords
Field
DocType
Time Complexity,Data Model,Significant Error,Minimum Variance,Versus Matrix
Hierarchical clustering,Diagonal,Econometrics,Minimum-variance unbiased estimator,Data modeling,Tree (graph theory),Matrix (mathematics),Statistics,Time complexity,Data model,Mathematics
Journal
Volume
Issue
ISSN
17
1
0176-4268
Citations 
PageRank 
References 
9
1.10
0
Authors
1
Name
Order
Citations
PageRank
Olivier Gascuel143376.01