404 CHAPTER10 Introduction to Weka The workbench includes methods for the main data mining problems: regression, classification, clustering, association rule mining, and attribute selection. Getting to know the data is an integral part of the work, and many data visualization facilities and data preprocessing tools are provided. All algorithms take their input in the form of a single relational table in the ARFF format described in Section 2.4, which can be read from a file or generated by a database query. One way of using Weka is to apply a learning method to a dataset and analyze its output to learn more about the data. Another is to use learned models to generate predictions on new instances. A third is to apply several different learners and compare their performance in order to choose one for prediction. In the interactive Weka interface, you select the learning method you want from a menu. Many methods have tunable parameters, which you access through a property sheet or object editor. A common evaluation module is used to measure the performance of all classifiers. Implementations of actual learning schemes are the most valuable resource that Weka provides. But tools for preprocessing the data, called filters, come a close second. Like classifiers, you select filters from a menu and tailor them to your requirements. We will show how different filters can be used, list the filtering algo-