Jacalyn M. Huband

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We have an m times n matrix D, and assume that its entries correspond to pair wise dissimilarities between m row objects O<sub>r</sub> and n column objects O<sub>c</sub>, 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(More)
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(More)
This paper addresses the relationship between the Visual Assessment of cluster Tendency (VAT) algorithm and single linkage hierarchical clustering. We present an analytical comparison of the two algorithms in conjunction with numerical examples to show that VAT reordering of dissimilarity data is directly related to the clusters produced by single linkage(More)
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(More)
The standard method for comparing gene products (proteins or RNA) is to compare their DNA or amino acid sequences. Additional information about some gene products may come from multiple sources, including the set of Gene Ontology (GO) annotations and the set of journal abstracts related to each gene product. Gene product similarity measures can be based on(More)