Data Mining Information About Data - 2882 Words

Abstract— Data Mining extracts useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian†¦show more content†¦The medical data processing has the high potential in medical domain for extracting the hidden patterns within the dataset [15]. These patterns are used for clinical diagnosis and prognosis. The medical data are generally distributed, heterogeneous and voluminous in nature. An important problem in medical analysis is to achieve the correct diagnosis of certain important information. This paper describes classification algorithms and it is used to analyze the performance of these algorithms. The accuracy measures are True Positive (TP) rate, F Measure, Receiver Operating Characteristics (ROC) area and Kappa Statistics. The error measures are Mean Absolute Error (M.A.E), Root Mean Squared Error (R.M.S.E), Relative Absolute Error (R.A.E) and Relative Root Squared Error (R.R.S.E) [5]. Section 2 explains the literature review; Section 3 describes the classification algorithms. Experimental results are analyzed in section 4 and section 5 illustrates the conclusion of this paper. II. LITERATURE REVIEW Dr. S.Vijayarani et al., [11] determined the performance of various classification techniques in data mining for predicting the heart disease from the heart disease dataset. The classification algorithms is used and tested in this work. The performance factors evaluate the efficiency of algorithms, clustering accuracy and error rate. The result illustrates LOGISTICS classification function efficiency is better than multilayer perception and sequential