Data mining techniques are widely used in classification and prediction in the field of bioinformatics to analyze biomedical data. The purpose of the study is to investigate and compare (7) different classification algorithms namely, Naive Bayes, Naive Bayes updatable, FT Tree, KStar, J48, LMT, and Neural network for analyzing Hepatitis prognostic data. The results of the classification are acc...

In data mining classification is to accurately predict the target class for each case in the data. Decision tree algorithm is one of the commonly used classification algorithm to make induction learning based on examples. In this paper we present the comparison of different classification techniques using WEKA. The aim of this paper is to investigate the performance of different classification ...

Data mining is the process of analyzing data from a different category. This data provide information and data mining will extracts a new knowledge from it and a new useful information is created. Decision tree learning is a method commonly used in data mining. The decision tree is a model of decision that looklike as a tree-like graph with nodes, branches and leaves. Each internal node denotes...

the Paper presents the comparison of different classification techniques for the task of classifying Number plate image data set. The comparison was conducted using WEKA (Waikato Environment for Knowledge Analysis) open source which mainly consists the collection of machine learning algorithms for data mining purpose. The main purpose of this paper is to investigate efficiency of different clas...

This research work deals with efficient data mining procedure for predicting the diabetes from medical records of patients. Diabetes is a very common disease these days in all populations and in all age groups. Diabetes contributes to heart disease, increases the risks of developing kidney disease, nerve damage, blood vessel damage and blindness. So mining the diabetes data in efficient manner ...

Study the behavior of malicious software, understand the security challenges, detect the malware behavior automatically using dynamic approach. Study various classification techniques and to group these malwares and able to cluster different malware into unknown group whose characteristics are not known. The classifiers used in this research are k-Nearest Neighbors (kNN), J48 Decision Tree, and...

Microarray analysis are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. This problem has received increased attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been used and co...