An Evolutionary Algorithm-Based Text Categorization Technique

In general, most of the organizations generate unstructured data from which extraction of meaningful information becomes a difficult task. Preprocessing of unstructured data before mining helps to improve the efficiency of the mining algorithms. In this paper, text data is initially preprocessed using tokenization, stop word removal, and stemming operations and a bag-of-words is identified to characterize the text dataset. Next, improved strength pareto evolutionary algorithm-based genetic algorithm is applied to determine the more compact set of informative words for clustering of text documents efficiently. It is a bi-objective genetic algorithm used to approximate the pareto-optimal front exploring the search space for optimal solution. The external clustering index and number of words described in the documents are considered as two objective functions of the algorithm, and based on these functions chromosomes in the population are evaluated and the best chromosome in non-dominated pareto front of final population gives the optimal set of words sufficient for categorizartion of text dataset.