Common document clustering algorithms utilize models that either divide a corpus into smaller clusters or gather individual documents into clusters. Hierarchical Agglomerative Clustering, a common gathering algorithm runs in O(n) to O(n) time, depending on the linkage of documents. In contrast, Bisecting K-Means Clustering has been shown to run in linear time with respect to the number of docum...

This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. Th...

This paper presents fingerprint indexing based on graph information of minutiae, fingerprint classification and verification based on hierarchical agglomerative clustering technique. The proposed fingerprint indexing is invariant under translation and rotation. Its performance is evaluated in terms of several real-life datasets. The fingerprint database is clustered into five classes based on t...

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 ...

In this paper, we describe our KBP Entity Lining system at TAC 2012. Our system consists of three modules. 1) Query expansion and candidate entity selection module. In this module, we identify all the possible entities for an entity mention through a variety knowledge sources. 2) Entity disambiguation module. In this module, we use a maximum margin approach to rank the candidate entity. 3) NIL ...

Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should refle...

Modularity is a popular measure for the quality of a cluster partition. Primarily, its popularity originates from its suitability for community identification through maximization. A lot of algorithms to maximize modularity have been proposed in recent years. Especially agglomerative hierarchical algorithms showed to be fast and find clusterings with high modularity. In this paper we present se...

INTRODUCTION Document clustering is an automatic grouping of text documents into clusters so that documents within a cluster have high similarity in comparison to one another, but are dissimilar to documents in other clusters. Unlike document classification (Wang, Zhou, and He, 2001), no labeled documents are provided in clustering; hence, clustering is also known as unsupervised learning. Hier...

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...

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...