Improving Computational Efficiency of Tentative Data Set
The aim of data mining is to find novel, interesting and useful patterns from data using algorithms that will do it in a way that is more computational efficient than previous method. Current data mining practice is very much driven by practical computational concerns. In focusing almost exclusively on computational issues; it is easy to forget that statistics is in fact a core component. The Conventional decision tree classifies the data whose values are known and particular, but in this paper such classifiers handle data with tentative information. The value vagueness arises in many applications during the data collection process.