Unique value disintegration for probing results using clustering algorithm

Abstract

This paper describes gene expression analysis by Unique Value Disintegration(UVD), emphasizing initial characterization of the data. We describe UVD methodsfor visualization of gene expression data, representation of the data using a smallernumber of variables, and detection of patterns in noisy gene expression data. Inaddition, we describe the precise relation between UVD analysis and PrincipalComponent Analysis (PCA) when PCA is calculated using the covariance matrix,enabling our descriptions to apply equally well to either method. Our aim is toprovide definitions, interpretations, examples, and references that will serve asresources for understanding and extending the application of UVD and PCA to geneexpression analysis.Search results clustering problem is defined as an automatic, on-line grouping of similar documents in a search results list returned from a search engine. In this paper we present Lingo—a novel algorithm for clustering search results, which emphasizes cluster description quality. We describe methods used in the algorithm: algebraic transformations of the term-document matrix and frequent phrase extraction using suffix arrays. Finally, we discuss results acquired from an empirical evaluation of the algorithm.