Fast Information-Theoretic Agglomerative Co-clustering

A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification

High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new informationtheoretic divisive algorithm for feature/word clustering and apply it to text classification. Exist...

Information Bottleneck Co-clustering

Co-clustering has emerged as an important approach for mining contingency data matrices. We present a novel approach to co-clustering based on the Information Bottleneck principle, called Information Bottleneck Co-clustering (IBCC), which supports both soft-partition and hardpartition co-clusterings, and leverages an annealing-style strategy to bypass local optima. Existing co-clustering method...

Comparison of Agglomerative and Partitional Document Clustering Algorithms

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters, and in greatly improving the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of do...

Fast Agglomerative Information Bottleneck Based Trajectory Clustering

Efficient Clustering and Matching for Object Class Recognition

In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering give...

Implementation of Hybrid Clustering Algorithm with Enhanced K-Means and Hierarchal Clustering

We are propose a hybrid clustering method, the methodology combines the strengths of both partitioning and agglomerative clustering methods. Clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularit...