The interface is OK, although with four to choose from, each with their own strengths, it can be awkward to choose which to work with, unless you have a thorough knowledge of the application to begin with. Overall, Weka is a good data mining tool with a comprehensive suite of algorithms. This app is written in Java and runs on almost any. Weka is a collection of machine learning algorithms for solving real-world data mining problems. Subsequently, it does not handle multi-relational mining and sequence modeling. Download Weka - latest version for Windows. It's core data mining algorithms include regression, clustering and classification.Īlthough Weka has a full suite of algorithms for data analysis, it has been built to handle data as single flat files. Weka's collection of algorithms range from those that handle data pre-processing to modeling. It is also appropriate for developing new machine learning schemes. Download and unzip the file wlsvm.zip Add the jars libsvm.jar and wlsvm.jar to your java project Use WLSVM as any other weka classifier Notes: Your java project should also have weka.jar. There are several ways of setting the options: Manually creating a String array: Using a single command-line string and using the splitOptions method of the class to turn it into an array: Using the OptionsToCode.java class to automatically turn a command line into code. WLSVM can be viewed as an implementation of the LibSVM running under Weka environment. The application contains the tools you'll need for data pre-processing, classification, regression, clustering, association rules, and visualization. Weka LibSVM (WLSVM) combines the merits of the two tools.
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