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Through the years researchers have crafted algorithms to carry out the process of object partitioning (clustering). All clustering algorithms ultimately rely on human inputs, principally in the form of the number of clusters to seek. This work investigates a new technique for automating cluster assessment and estimating the number of clusters to look for in unlabeled data utilizing the VAT [visual...
Many important applications in biology have underlying datasets that are relational, that is, only the (dis)similarity between biological objects (amino acid sequences, gene expression profiles, etc.) is known and not their feature values in some feature space. Examples of such relational datasets are the gene similarity matrices obtained from BLAST, gene expression data, or gene ontology (GO) similarity...
We have an m times n matrix D, and assume that its entries correspond to pair wise dissimilarities between m row objects Or and n column objects Oc, which, taken together (as a union), comprise a set O of N = m + n objects. This paper develops a new visual approach that applies to four different cluster assessment problems associated with O. The problems are the assessment of cluster tendency: PI)...
Successes with kernel-based classification methods have spawned recent efforts to kernelize clustering algorithms for object data. Here we extend the kernelization to relational data clustering by proposing a kernelized form of the nonEuclidean relational fuzzy c-means algorithm. A numerical test result is included
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