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A general strategy for the simultaneous classification of variables and objects in ecological data tables
Author(s) -
Podani János,
Feoli Enrico
Publication year - 1991
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.2307/3236025
Subject(s) - cluster analysis , entropy (arrow of time) , block (permutation group theory) , mathematics , computer science , matrix (chemical analysis) , measure (data warehouse) , data mining , algorithm , statistics , combinatorics , physics , materials science , quantum mechanics , composite material
A general iterative algorithm is proposed for the arrangement of data matrices by optimizing X 2 , sum of squares or pooled entropy for blocks determined by clusters of variables and objects. The method includes k ‐means clustering and constrained block clustering as special cases. Possibilities for evaluating the resulting matrix include calculation of relative contributions to measures of block sharpness, comparison of partitions and construction of consensus tables. A new measure for comparing rearranged data matrices is suggested. The distributional properties of final results produced under different starting conditions are examined. The performance of the algorithm is tested on binary vegetation data from the Italian Alps.