How many clusters are best?—an experiment
Pattern Recognition
Pattern classification with genetic algorithms: incorporation of chromosome differentiation
Pattern Recognition Letters
The handbook of brain theory and neural networks
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Mean Shift Is a Bound Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Signature Authentication Scheme using Integrated Statistical Analysis of Bi-Color Images
ICCSA '07 Proceedings of the The 2007 International Conference Computational Science and its Applications
An improved algorithm for clustering gene expression data
Bioinformatics
Handwritten Signature Authentication Using Statistical Estimation
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
Gene Identification: Classical and Computational Intelligence Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we propose handwritten signature classification using a supervised chromosome clustering technique. Due to the time variant nature of handwriting of human being, a set of hundred sample handwritten signatures were first collected from the user or individual in form of the same sized grayscale images. These grayscale handwritten signature images will be used as the training set in our classification algorithm. Our proposed algorithm will then decide whether the future incoming handwritten signature of an individual can be a member of the training set or not. In this paper, the distance and similarities play an important role, where the greater the dissimilarity measure or distance of genes, the more dissimilar are the two chromosomes.