Undirect Knowledge Discovery By UsingSingular Value Decomposition
Ekaterina Maltseva, Clara Pizzuti, Domenico Talia · WIT transactions on information and communication technologies · 2000
Clustering is an undirected knowledge discovery technique based on the partitioning of large sets of data objects into homogenous groups. All objects contained in the same group have similar characteristics. Grouping multivariate data is a difficult data mining task when no domain knowledge on data structure is available. In this paper we describe the use of a well known linear projection technique, called singular value decomposition (SVD), to discover clusters in the pattern space by projecting it into a subspace that constitutes its best approximation and preserves the character of data. Experimental results on real datasets from the UCI Machine Learning repository assess the quality of the clustering obtained.