NDLI: Visualization of high-dimensional data using an association of multidimensional scaling to clustering

Content Provider	IEEE Xplore Digital Library
Author	Naud, A.
Copyright Year	2004
Description	Author affiliation: Dept. of Informatics, Nicolaus Copernicus Univ., Toruri, Poland (Naud, A.)
Abstract	A common task in data mining is the visualization of multivariate objects on scatterplots, allowing human observers to perceive subtle inter-relations in the dataset such as outliers, groupings or other regularities. Multidimensional scaling (MDS) is a well known exploratory data analysis family of techniques that produce one display on which inter-object similarity relationships are preserved. The algorithm scales with the square of the number of visualized data, which limits its application to small datasets. In order to alleviate this limitation, we associate MDS with three different clustering models, namely the learning vector quantization, the k-means and the dendrograms. We propose to perform dimensionality reduction on a reduced set of cluster centers, to which the data are added using a relative MDS mapping. Our experiments show that this approach allows to obtain displays of large datasets with fairly good visualization properties, when compared with the display obtained by a direct mapping of the whole dataset.
Starting Page	252
Ending Page	255
File Size	724535
Page Count	4
File Format	PDF
ISBN	0780386434
DOI	10.1109/ICCIS.2004.1460421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-01
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data visualization Multidimensional systems Displays Data analysis Clustering algorithms Neural networks Informatics Data mining Scattering Humans
Content Type	Text
Resource Type	Article

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