loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: João M. M. Duarte 1 ; Ana L. N. Fred 2 and F. Jorge F. Duarte 3

Affiliations: 1 Polytechnic of Porto (ISEP/IPP) and Instituto Superior Técnico, Portugal ; 2 Instituto Superior Técnico, Portugal ; 3 Polytechnic of Porto (ISEP/IPP), Portugal

Keyword(s): Constrained Data Clustering, Clustering Combination, Unsupervised Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

Abstract: Recent work on constrained data clustering have shown that the incorporation of pairwise constraints, such as must-link and cannot-link constraints, increases the accuracy of single run data clustering methods. It was also shown that the quality of a consensus partition, resulting from the combination of multiple data partitions, is usually superior than the quality of the partitions produced by single run clustering algorithms. In this paper we test the effectiveness of adding pairwise constraints to the Evidence Accumulation Clustering framework. For this purpose, a new soft-constrained hierarchical clustering algorithm is proposed and is used for the extraction of the consensus partition from the co-association matrix. It is also studied whether there are advantages in selecting the must-link and cannot-link constraints on certain subsets of the data instead of selecting these constraints at random on the entire data set. Experimental results on 7 synthetic and 7 real data sets ha ve shown the use of soft constraints improves the performance of the Evidence Accumulation Clustering. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.233.219.31

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
M. M. Duarte, J.; L. N. Fred, A. and F. Duarte, F. (2012). Evidence Accumulation Clustering using Pairwise Constraints. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR; ISBN 978-989-8565-29-7; ISSN 2184-3228, SciTePress, pages 293-299. DOI: 10.5220/0004171902930299

@conference{kdir12,
author={João {M. M. Duarte}. and Ana {L. N. Fred}. and F. Jorge {F. Duarte}.},
title={Evidence Accumulation Clustering using Pairwise Constraints},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR},
year={2012},
pages={293-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004171902930299},
isbn={978-989-8565-29-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR
TI - Evidence Accumulation Clustering using Pairwise Constraints
SN - 978-989-8565-29-7
IS - 2184-3228
AU - M. M. Duarte, J.
AU - L. N. Fred, A.
AU - F. Duarte, F.
PY - 2012
SP - 293
EP - 299
DO - 10.5220/0004171902930299
PB - SciTePress