Data Mining Information About Data

2882 Words Feb 16th, 2015 12 Pages
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 and Lazy classifiers for hepatitis dataset. In Bayesian classifier there are two algorithms namely BayesNet and NaiveBayes. In Lazy classifier we have two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool. It is open source software which consists of the collection of machine learning algorithms for data mining tasks.
Keywords— Data Mining, Classification, Bayesian, Lazy, BayesNet, NaiveBayes, IBK, KStar.
I. INTRODUCTION
Data mining refers to extracting knowledge from massive amount of information. It is the set of activities used to find new, hidden or unexpected patterns or unusual patterns in data. Compared with other data mining application fields, medical data mining plays an important role and it…
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