Enhancing text clustering model based on Truncated Singular Value Decomposition, fuzzy ART and Cross Validation

Numerical schemes research on clustering model has been quite intensive in the past decade. The difficulties associated with curse of dimensionality and cost functions to reflect the general knowledge about internal structures and distributions of target data. Traditional computational clustering and variables selection schemes are struggling to estimate at high level of accuracy for this type of problem. Hence, in the present study, a novel semantic-based scheme was proposed to enhance the clustering accuracy. The results show that our conceptual model is automatic and optimal. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach.