A Data Clustering Based on MDL Criterion

A Successive State Splitting Algorithm Based on the Mdl Criterion by Data-driven and Decision Tree Clustering

We propose a new Successive State Splitting (SSS) algorithm based on the Minimum Description Length (MDL) criterion to design tied-state HMM topologies automatically. The SSS algorithm is a mechanism for creating both temporal and contextual variations based on the Maximum Likelihood (ML) criterion. However, it also needs to empirically predetermine control parameters for use as stop criteria, ...

Construction of nonlinear discrimination function based on the MDL criterion

Although a nonlinear discrimination function may be superior to linear or quadratic classiiers, it is diicult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legenedre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real...

An Mdl Framework for Data Clustering

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Speaker Clustering Based on Bayesian Information Criterion

This paper presents an effective method for clustering unknown speech utterances based on their associated speakers. The proposed method jointly optimizes the generated clusters and the number of clusters according to a Bayesian information criterion (BIC). The criterion assesses a partitioning of utterances based on how high the level of withincluster homogeneity can be achieved at the expense...