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Text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting interesting and non-trivial patterns or knowledge from text documents. Regarded by many as the next wave of knowledge discovery, text mining has very high commercial values. Text Mining can be achieved directly by applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. The various researches show the performance is improved due to the nature of Genetic Algorithm. Genetic Algorithm has been used to tackle extensive variety of optimization problems. In this paper text mining concept is discussed and how genetic algorithm is applied on it and its usage in text mining.
Text Mining is the discovery of new previously unknown information, by automatically extracting information from a usually large amount of data set. Many algorithms have been developed in recent years for solving problems of numerical and combinatorial optimization problems. Most efficient among them are swarm intelligence algorithms. Clustering using PSO is being used as an alternative to more conventional clustering techniques. PSO is population-based stochastic search algorithms that impersonate the capability of swarm. Data clustering with PSO algorithms are being used to produce better outcomes in a wide variety of real-world data. In this paper, a brief survey on PSO application in data clustering is described.