KNOWLEDGE DISCOVERY and SAMPLING TECHNIQUES with DATA MINING for IDENTIFYING TRENDS in DATA SETS

Abstract- Complex search and data analysis can be automated using Knowledge Discovery. Extracting hidden knowledge in large volume of raw data can be accomplished by exploiting theoretical and practical issue in Data Mining techniques for Knowledge Discovery. Complex iterative and sequential steps formulate Knowledge Discovery in Databases. The performance of Data mining tools may be degraded due to poor training set and bulky practical data set. The Data Mining process can be optimized by focusing on the efficient method to estimate optimal training set. It is looked upon as a quite challenging problem. In our work, we are aiming to focus on Knowledge Discovery techniques by improving approach with efficiently identifying the sampling dataset and data mining algorithm to mine the sampled data