Data mining classification problem of clustering is improved with the help of K-Mean++ Algorithm

Abstract

The goal of data mining is to extract or “mine" knowledge from large amounts of data. Knowledge and understanding of a problem is always the first step in identifying effective solutions. However, data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data .KDD process assumes that all the data is easily accessible at a central location or through centralized access mechanisms such as federated databases and virtual ware houses .

The application of data mining techniques on official data has great potential in supporting good public policy. It’s a technique can be used to detect errors in data collection, cluster, classify, make prediction, and generate interesting association patterns of survey databases.

Recommender systems based on automated collaborative filtering predict new items of interest for a user based on predictive relationships discovered between that user and other participants of a community. Most

of the successful research and commercial systems in collaborative filtering use a nearest-neighbor model Process of semi-automatically analyzing large databases to find interesting and useful patterns. Overlaps

for generating predictions. Automated collaborative filtering systems based on the nearest-neighbor method work in three simple phases