We propose a novel method for clustering allophones called Feature-Dependent Allophone Clustering (FD-AC) that determines feature-dependent HMM topology automatically. Existing methods for allophone clustering are based on parameter sharing between the allophone models that resemble each other in behaviors of feature vector sequences. However, all the features of the vector sequences may not ne...

Clustering is an important data mining task Data mining often concerns large and high dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high dimensionality or both Di erent features a ect clusters di erently some are important for clusters while others may hinder the clustering task An e cient way of handling it is by selecting ...

In this paper we propose and test the use of hierarchical clustering for feature selection in databases. The clustering method is Ward’s with a distance measure based on Goodman-Kruskal τ . We motivate the choice of this measure and compare it with other ones. Our hierarchical clustering is applied to over 40 data-sets from UCI archive. The proposed approach is interesting from many viewpoints....

In this thesis, we present a new clustering algorithm we call Significance Feature Clustering, which is designed to cluster text documents. Its central premise is the mapping of raw frequency count vectors to discrete-valued significance vectors which contain values of -1, 0, or 1. These values represent whether a word is significantly negative, neutral, or significantly positive, respectively....