Determining the Number of Probability-Based Clustering: A Hybrid Approach

摘要

While analyzing the previous methods for determining the number of probability-based clustering, this paper introduces an improved Monte Carlo Cross-Validation algorithm (iMCCV) and attempts to solve the posterior probabilities spread problem, which cannot be resolved by the Monte Carlo Cross-Validation algorithm. Furthermore, we present a hybrid approach to determine the number of probability-based clustering by combining the iMCCV algorithm and the parallel coordinates visual technology. The efficiency of our approach is discussed with experimental results.