INFORMATICA International Journal Main Page Editorial Board Abstracting/Indexing Instructions to Authors Subscription Information Contents Author Index Papers in Production		INFORMATICA, 2007, Vol. 18, No. 2, 187-202 © Institute of Mathematics and Informatics, ISSN 0868-4952 Conditions for Optimal Efficiency of Relative MDS Jolita BERNATAVICIENE, Gintautas DZEMYDA, Virginijus MARCINKEVICIUS Institute of Mathematics and Informatics Akademijos 4, 08663 Vilnius, Lithuania E-mail: {jolitab, dzemyda, virgism}@ktl.mii.lt Abstract In this paper, the relative multidimensional scaling method is investigated. This method is designated to visualize large multidimensional data. The method encompasses application of multidimensional scaling (MDS) to the so-called basic vector set and further mapping of the remaining vectors from the analyzed data set. In the original algorithm of relative MDS, the visualization process is divided into three steps: the set of basis vectors is constructed using the k-means clustering method; this set is projected onto the plane using the MDS algorithm; the set of remaining data is visualized using the relative mapping algorithm. We propose a modification, which differs from the original algorithm in the strategy of selecting the basis vectors. The experimental investigation has shown that the modification exceeds the original algorithm in the visualization quality and computational expenses. The conditions, where the relative MDS efficiency exceeds that of standard MDS, are estimated. Keywords: multidimensional scaling, visualization, clustering, relative MDS, large data sets, data mining To preview Lithuanian abstract see full article text To preview full article text in PDF format click here You could obtain free Acrobat Reader from Adobe
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