Maximal Interaction Two-Mode Clustering

Two-mode Clustering

A Bayesian Approach to Two-Mode Clustering∗

We develop a new Bayesian approach to estimate the parameters of a latent-class model for the joint clustering of both modes of two-mode data matrices. Posterior results are obtained using a Gibbs sampler with data augmentation. Our Bayesian approach has three advantages over existing methods. First, we are able to do statistical inference on the model parameters, which would not be possible us...

Global Optimization Strategies for Two-Mode Clustering∗

Two-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k -means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode cl...

Scalability of Parallel Genetic Algorithm for Two-mode Clustering

Data matrix having the same set of entity in the rows and cloumns is known as one-mode data matrix, and traditional one-mode clustering algorithms can be used to cluster the rows (or columns) separately. With the popularity of use of two-mode data matrices where the rows and columns have different sets of entities, the need for simultaneous clustering of rows and columns popularly known as two-...

Clustering Documents with Maximal Substrings

This paper provides experimental results showing that we can use maximal substrings as elementary building blocks of documents in place of the words extracted by a current state-of-the-art supervised word extraction. Maximal substrings are defined as the substrings each giving a smaller number of occurrences even by appending only one character to its head or tail. The main feature of maximal s...