Performance Analysis of K-Mean Clustering on Normalized and Un-Normalized Information in Data Mining

Abstract: The research includes the concept of data mining, clustering and clustering techniques. Data mining is used for extract the useful information and Clustering is the concept used to groups which can be creating by identifying similar kind of data and this can done by identify one or more attributes or classes. There are different types of clustering techniques such as K-Means clustering, K-Means Clustering, etc. The analysis has been done using the K-Means Clustering technique and by normalizes the data using data mining normalization techniques. Furthermore, the research work is about the study of data such as Normalized data and Un-normalized Data and analyzes the Data using Clustering Algorithm such as K-Means Clustering algorithm. The data mining means extract the useful information from the large dataset and clusters the records. The basic principles of data mining is to analyze the data from different angle, categorize it and finally to summarize it. The need for data mining is that there have been too much data, too much technology but don�t have useful information. Data clustering is a process of putting similar data into groups.

Keywords: Data Mining, K-Means clustering algorithm, Normalized data, Un-normalized Data.