The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...

Introduction Primary biological aerosol particle (PBAP) classification requires discrimination of particles various diverse sources which may have wide reaching effects in the atmosphere. In order to predict these effects under future emissions scenarios it is useful to be able to identify ambient PBAP concentration. To date, this has largely been achieved by the use of off-line techniques, whi...

Agglomerative hierarchical speaker clustering (AHSC) has been widely used for classifying speech data by speaker characteristics. Its bottom-up, one-way structure of merging the closest cluster pair at every recursion step, however, makes it difficult to recover from incorrect merging. Hence, making AHSC robust to incorrect merging is an important issue. In this paper we address this problem in...

An efficient shot summarization method is presented based on agglomerative clustering of the shot frames. Unlike other agglomerative methods, our approach relies on a cluster merging criterion that computes the content homogeneity of a merged cluster. An important feature of the proposed approach is the automatic estimation of the number of a shot's most representative frames, called keyframes....

The application of clustering methods for automatic taxonomy construction from text requires knowledge about the tradeoff between, (i), their effectiveness (quality of result), (ii), efficiency (run-time behaviour), and, (iii), traceability of the taxonomy construction by the ontology engineer. In this line, we present an original conceptual clustering method based on Formal Concept Analysis fo...

A distributed memory parallel version of the group average Hierarchical Agglomerative Clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard TREC test collection, our par...

A multi-step method of partitioning the pixels of an image such that the partitions at one step are wholly nested inside the partitions of the next step is described, i.e. we describe an agglomerative, hierarchical segmentation technique that uses texture information to perform the segmentation. The image is requantized using K-Means clustering. Then, clusters are expanded using region growing ...