The Texture Fragmentation and Reconstruction (TFR) algorithm has been recently introduced [9] to address the problem of image segmentation by textural properties, based on a suita...
In this paper, an evolutionary clustering technique is described that uses a new point symmetry-based distance measure. The algorithm is therefore able to detect both convex and n...
Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the ...
We present a clustering technique addressing redundancy for bounded-distance clusters, which means being able to determine the minimum number of cluster-heads per node, and the ma...
We propose a new method for clustering based on finding maximum margin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, ...
Linli Xu, James Neufeld, Bryce Larson, Dale Schuur...
The relationship between XML data clustering and schema matching is bidirectional. On one side, clustering techniques have been adopted to improve matching performance, and on the...
Decomposing a software system into smaller, more manageable clusters is a common approach to support the comprehension of large systems. In recent years, researchers have focused ...
When data resides on tertiary storage, clustering is the key to achieving high retrieval performance. However, a straightforward approach to clustering massive amounts of data on ...
Abstract. This paper presents an innovative, adaptive variant of Kohonen’s selforganizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on ...
In solving the classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets sto...