Large high dimensional data handling using data reduction

This paper represent recent problems of computer based applications like medical diagnosis or prediction, weather prediction, climate prediction, text document classification, finance and market data prediction etc. Now a days, as data availability increase, data size in terms of samples/instances and attributes are increase which are called large and high dimensional dataset respectively. In most of the applications, data increase in both cases called Large High Dimensional data. Data mining methods which are used for data analysis, classification, clustering and prediction on this type of data, mining methods degrade its performance. So, handling such dataset, data reduction play important role in pre-process steps with preserve or increase performance. Data reduction reduce samples by instance reduction or Horizontal reduction and attributes by attribute/feature reduction or Vertical reduction. Data reductions methods are depend on for which data mining method using. In this paper, classification method is considered with data reduction. For proving importance of data reduction, here weighted k-nearest neighbor classifier is used. Uniform random sampling selection is used for data reduction in both direction-instances and attributes and results show that after data reduction, Accuracy is preserve or increase in most of cases and execution time is decrease in all cases.