A new cluster isolation criterion based on dissimilarity increments
A. Fred, and J. Leitao. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25 (8):
944--958(2003)
Abstract
This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these increments by an exponential density; we propose to use this statistical model, which characterizes context, to derive a new cluster isolation criterion. The integration of this criterion in a hierarchical agglomerative clustering framework produces a partitioning of the data, while exhibiting data interrelationships in terms of a dendrogram-type graph. The analysis of the criterion is undertaken through a set of examples, showing the versatility of the method in identifying clusters with arbitrary shape and size; the number of clusters is intrinsically found without requiring ad hoc specification of design parameters nor engaging in a computationally demanding optimization procedure.
%0 Journal Article
%1 citeulike:257249
%A Fred, A. L. N.
%A Leitao, J. M. N.
%D 2003
%J Pattern Analysis and Machine Intelligence, IEEE Transactions on
%K clustering contraint similarity
%N 8
%P 944--958
%T A new cluster isolation criterion based on dissimilarity increments
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1217600
%V 25
%X This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these increments by an exponential density; we propose to use this statistical model, which characterizes context, to derive a new cluster isolation criterion. The integration of this criterion in a hierarchical agglomerative clustering framework produces a partitioning of the data, while exhibiting data interrelationships in terms of a dendrogram-type graph. The analysis of the criterion is undertaken through a set of examples, showing the versatility of the method in identifying clusters with arbitrary shape and size; the number of clusters is intrinsically found without requiring ad hoc specification of design parameters nor engaging in a computationally demanding optimization procedure.
@article{citeulike:257249,
abstract = {This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these increments by an exponential density; we propose to use this statistical model, which characterizes context, to derive a new cluster isolation criterion. The integration of this criterion in a hierarchical agglomerative clustering framework produces a partitioning of the data, while exhibiting data interrelationships in terms of a dendrogram-type graph. The analysis of the criterion is undertaken through a set of examples, showing the versatility of the method in identifying clusters with arbitrary shape and size; the number of clusters is intrinsically found without requiring ad hoc specification of design parameters nor engaging in a computationally demanding optimization procedure.},
added-at = {2006-06-16T10:34:37.000+0200},
author = {Fred, A. L. N. and Leitao, J. M. N.},
biburl = {https://www.bibsonomy.org/bibtex/293b5feb1d6d95b4aae380b3e45834b0b/ldietz},
citeulike-article-id = {257249},
interhash = {d3b7a2e952a29e199df17d339de52203},
intrahash = {93b5feb1d6d95b4aae380b3e45834b0b},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
keywords = {clustering contraint similarity},
number = 8,
pages = {944--958},
priority = {4},
timestamp = {2006-06-16T10:34:37.000+0200},
title = {A new cluster isolation criterion based on dissimilarity increments},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1217600},
volume = 25,
year = 2003
}