• L BUJJI BABU
• KSR PRASAD
• E RAMA LAKSHMI
• B NANDAN KUMAR

Feature subset selection is an effective way for dimensionality reduction, eliminating irrelevant data and redundant data, increasing accuracy. There are various feature subset selection methods in machine learning applications and they are classified into four categories: Embedded, wrapper, filter and hybrid approaches. Embedded approach is more efficient than other three approaches. Example for this approach is traditional machine learning algorithms such as decision trees and neural networks. Wrapper method gives more accuracy in learning algorithms. But here the computational complexity is large. This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified. The goal of supervised clustering is to identify class-uniform clusters that have high probability densities. A novel approach called supervised attribute clustering algorithm is proposed to improve the accuracy and check the probability of the patterns. In this method, faster retrieval of relevant data is made more efficient and accurate. By using this method, users can get precise results and negligible data loss. This method displays results based on the high probability density thereby providing privacy for data and reducing the dimensionality of the data

Embedded, Wrapper, Clustering, Hybrid, Virginica, Setosa

"A Novel performance of Supervised attribute clustering of dimensionality reduction in irrelevant data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.536-540, November-2018, Available :http://www.jetir.org/papers/JETIR1811070.pdf

"A Novel performance of Supervised attribute clustering of dimensionality reduction in irrelevant data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp536-540, November-2018, Available at : http://www.jetir.org/papers/JETIR1811070.pdf