Data Mining and Knowledge Discovery Handbook, 2 Edition part 130. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 1270 Eibe Frank et al. Fig. . The Explorer Interface. to compare different methods and identify those that are most appropriate for the problem at hand. The workbench includes methods for all the standard Data Mining problems regression classification clustering association rule mining and attribute selection. Getting to know the data is is a very important part of Data Mining and many data visualization facilities and data preprocessing tools are provided. All algorithms and methods take their input in the form of a single relational table which can be read from a file or generated by a database query. Exploring the Data The main graphical user interface the Explorer is shown in Figure . It has six different panels accessed by the tabs at the top that correspond to the various Data Mining tasks supported. In the Preprocess panel shown in Figure data can be loaded from a file or extracted from a database using an SQL query. The file can be in CSV format or in the system s native ARFF file format. Database access is provided through Java Database Connectivity which allows SQL queries to be posed to any database for which a suitable driver exists. Once a dataset has been read various data preprocessing tools called filters can be applied for example numeric data can be discretized. In Figure the user has loaded a data file and is focusing on a particular attribute normalized-losses examining its statistics and a histogram. Through the Explorer s second panel called Classify classification and regression algorithms can be applied to the preprocessed data. This panel also enables users to evaluate the resulting models both numerically through statistical estimation and graphically through visualization of the data and examination of the model if the model structure is amenable to visualization . Users can also load and save models. 66 Weka-A Machine Learning Workbench for Data Mining 1271 Fig. . The Knowledge Flow Interface. The third panel .
