Growth of Computer applications in most of the people and companies are wanted to work through computers. They mostly use computer to store and retrieve information. Data mining is organizing and retrieving information from large data set. Now a day’s dataset may be dynamic. Text Document clustering is a passion or an interested area of data mining. Many of the clustering method needed for a new one requires better clustering approaches. A new proposal is an improved KLOA with feature selection method for text mining that is Improved KLOA. K-means is one of the active algorithms for wider application of clustering technique. But it has some inconvenience to form a cluster in the initial point. A novel KLOA algorithm is refined and enhanced by k-means algorithm. This is used to pick the initial point and perform well when some think is rendered. To implement Feature selection method is to find subset and improve the process of cluster. Using Feature selection method is to improve the quality of cluster and find intrinsic properties of dataset. In this new article using wrapper technique of feature selection method is implemented and produces high quality of text clusters, with more accuracy and performance.

Clustering Technique, Data mining, Feature selection, Optimization, Text Clustering

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