In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but als...
: The increasing number of digitized texts presently available notably on the Web has developed an acute need in text mining techniques. Clustering systems are used more and more o...
Abdelmalek Amine, Zakaria Elberrichi, Michel Simon...
—Many registration scenarios involve aligning more than just two images. These image sets—called ensembles—are conventionally registered by choosing one image as a template, ...
This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE). This approach creates neural network ensembles in an innovative ...
Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial c...
This paper presents a framework for clustering in text-based information retrieval systems. The prominent feature of the proposed method is that documents, terms, and other relate...
In this paper, we describe a document clustering method called noveltybased document clustering. This method clusters documents based on similarity and novelty. The method assigns...
Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity meas...
Current studies on the storage of XML data are focused on either the efficient mapping of XML data onto an existing RDBMS or the development of a native XML storage. Some native X...
Background: There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large,...