A Divisive Clustering for Mixed Feature-Type Symbolic Data

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...

Dissimilarity measures for histogram-valued data and divisive clustering of symbolic objects

Contemporary datasets are becoming increasingly larger and more complex, while techniques to analyse them are becoming more and more inadequate. Thus, new methods are needed to handle these new types of data. This study introduces methods to cluster histogram-valued data. However, histogram-valued data are difficult to handle computationally because observations typically have a different numbe...

Dependency-based feature selection for clustering symbolic data

Divisive clustering of high dimensional data streams

Clustering streaming data is gaining importance as automatic data acquisition technologies are deployed in diverse applications. We propose a fully incremental projected divisive clustering method for high-dimensional data streams that is motivated by high density clustering. The method is capable of identifying clusters in arbitrary subspaces, estimating the number of clusters, and detecting c...

MK-Prototypes: A Novel Algorithm for Clustering Mixed Type Data