Software Engineering

Weka - Data Mining Tool  Algorithms: There are a lot of algorithm in weka for various classification and experiments and some the major and widely used are following : Decision tree(J48): NAME: weka.classifiers.trees.J48 SYNOPSIS: Class for generating a pruned or Un pruned  C4.5 decision tree. Naïve Bayes: NAME: weka.classifiers.bayes.NaiveBayes SYNOPSIS: Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) KNN(IBK): NAME: weka.classifiers.lazy.IBk SYNOPSIS: K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting. SVM(LibSVM):  NAME: weka.classifiers.functions.LibSVM SYNOPSIS: A wrapper class for the libsvm tools (the li...

Weka - Data Mining Tool Data Sets You can download datasets from  Here   (eg. Adult, Irish, Zoo etc used here) and save the datasets in to “.arff” format. All the datasets are in numeric value format so I changed it into Nominal values in order to process by algorithms used in Weka by following process:          Click Open file in order to open dataset file eg. Adult.arff          Select Choose > Filter > Unsupervised >attribute> NumericToNominal          Click All to apply change to all  attributes          Click Apply          See the Result > Type: Nominal         Click Save to save the result eg. Adult.arff Furthermore, we can found number of attribute as 15, and instances as 32562 and Relation as conversion to Nominal. Same Procedure applied to all data sets for eg: In irish , we can found ...

Weka - Data mining Tool W eka is a tool for big data and data mining. It is used to various classification, experiments, and analysis over large data sets. Installation Guide -weka  You can download Weka from   here   and follow the normal installation procedure. After completion you will get following window, here you can begin your classification or experiment on different data sets with Weka.