A Fast Clustering – Based High-Dimensional Data by Using Text Classification

Most existing popular text segregation methods have adopted term-based approaches. It classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. The field of text mining seeks to extract useful information from unstructured textual data through the identification and exploration of interesting patterns. The discovery of relevant features in real-world data for describing user information needs or preferences is a new challenge in text mining. Relevance of a feature indicates that the features is always necessary for an optimal subset, it cannot be removed without affecting the original conditional class distribution. In this paper, an adaptive method for relevance feature discovery is discussed, to find useful features available in a feedback set, including both positive and negative documents, for describing what users need. Thus, this paper discusses the methods for relevance feature discovery using the simulated annealing approximation and genetic algorithm, a population of candidate solutions to an optimization problem toward better solutions.