Authors:
Veselka Boeva
1
;
Milena Angelova
2
and
Elena Tsiporkova
3
Affiliations:
1
Computer Science and Engineering Dept., Blekinge Institute of Technology, Karlskrona and Sweden
;
2
Computer Systems and Technologies Dept., Technical University of Sofia, Plovdiv and Bulgaria
;
3
The Collective Center for the Belgian Technological Industry, Brussels and Belgium
Keyword(s):
Data Mining, Evolutionary Clustering, Bipartite Clustering, PubMed Data, Unsupervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
In this article we propose a bipartite correlation clustering technique that can be used to adapt the existing clustering solution to a clustering of newly collected data elements. The proposed technique is supposed to provide the flexibility to compute clusters on a new portion of data collected over a defined time period and to update the existing clustering solution by the computed new one. Such an updating clustering should better reflect the current characteristics of the data by being able to examine clusters occurring in the considered time period and eventually capture interesting trends in the area. For example, some clusters will be updated by merging with ones from newly constructed clustering while others will be transformed by splitting their elements among several new clusters. The proposed clustering algorithm, entitled Split-Merge Evolutionary Clustering, is evaluated and compared to another bipartite correlation clustering technique (PivotBiCluster) on two different
case studies: expertise retrieval and patient profiling in healthcare.
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