Algorithms for clustering data
Algorithms for clustering data
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Clustering Algorithms
Introduction to Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Algorithm Design
GAKREM: A novel hybrid clustering algorithm
Information Sciences: an International Journal
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Cluster Analysis
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
DNA sequence comparison by a novel probabilistic method
Information Sciences: an International Journal
Inducing decision trees from medical decision processes
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Automatic threshold estimation for data matching applications
Information Sciences: an International Journal
SMART: Stream Monitoring enterprise Activities by RFID Tags
Information Sciences: an International Journal
On the combination of relative clustering validity criteria
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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A conceptual problem that appears in different contexts of clustering analysis is that of measuring the degree of compatibility between two sequences of numbers. This problem is usually addressed by means of numerical indexes referred to as sequence correlation indexes. This paper elaborates on why some specific sequence correlation indexes may not be good choices depending on the application scenario in hand. A variant of the Product-Moment correlation coefficient and a weighted formulation for the Goodman-Kruskal and Kendall's indexes are derived that may be more appropriate for some particular application scenarios. The proposed and existing indexes are analyzed from different perspectives, such as their sensitivity to the ranks and magnitudes of the sequences under evaluation, among other relevant aspects of the problem. The results help suggesting scenarios within the context of clustering analysis that are possibly more appropriate for the application of each index.