A classification method to classify high dimensional data

The rapid computerization and advancement in the technology has led to huge amount of data in the databases. Research has shown that the amount of data in the world doubles in every 20 months. However, this available data consists of large number of noise values and thus, cannot be directly used. The extraction of information from the vast pool of data has emerged a major challenge. Machine learning techniques have emerged as an effective tool to overcome this challenge. Several machine learning algorithms (like SVM, K-means etc.) are effectively applied in data mining. In this paper author have applied classification and clustering techniques on different datasets and have proposed a model for enhancing the performance of K-means data clustering method and Naïve Bayes data classification method. The efficiency of the proposed model is calculated based on general parameters like accuracy, precision, recall, F-measure and number of iterations.