Adecision tree can be used not only as a classifier but also as a clustering method. One of such applications can be found in automatic speech recognition using hidden Markov models (HMMs). Due to the insufficient amount of training data, similar states of triphone HMMs are grouped together using a decision tree to share a common probability distribution. At the same time, in order to predict the statistics of unseen triphones, the decision tree is used as a classifier as well. In this paper, we study several cluster split criteria in decision tree building algorithms for the case where the instances to be clustered are probability density functions. Especially, when Gaussian probability distributions are to be clustered, we have found that the Bhattacharyya distance based measures are more consistent than the conventional log likelihood based measure.
CITATION STYLE
Yook, D. (2002). Decision tree based clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2412, pp. 487–492). Springer Verlag. https://doi.org/10.1007/3-540-45675-9_73
Mendeley helps you to discover research relevant for your work.