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The Windows databases article explains how to do this. * For MS Access, you must use the JDBC-ODBC-bridge that is part of a JDK.
#CONFIGURE WEKA JAR DRIVER#
* Don't forget to add the JDBC driver to your CLASSPATH. Instances data = query.retrieveInstances() You can declare that your data set is sparse InstanceQuery query = new InstanceQuery()
#CONFIGURE WEKA JAR CODE#
Secondly, your Java code needs to look like this to load the data from the database: import JdbcURL=jdbc:mysql://localhost:3306/some_database Your props file must contain the following lines: jdbcDriver=.mysql.Driver
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Since you're only reading, you can use the default user nobody without a password. (The driver class is .mysql.Driver.) The database where your target data resides is called some_database. The MySQL JDBC driver is called Connector/J.
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Suppose you want to connect to a MySQL server that is running on the local machine on the default port 3306. First, you'll have to modify your DatabaseUtils.props file to reflect your database connection. Reading from Databases is slightly more complicated, but still very easy. For example, the XRFF format saves the class attribute information as wellĭata.setClassIndex(data.numAttributes() - 1) setting class attribute if the data format does not provide this information import .DataSource ĭataSource source = new DataSource("/some/where/data.arff") It can also read CSV files and other formats (basically all file formats that Weka can import via its converters it uses the file extension to determine the associated loader). The DataSource class is not limited to ARFF files. The classifiers and filters always list their options in the Javadoc API ( stable, developer version) specification.Ī comprehensive source of information is the chapter Using the API of the Weka manual. A link to an example class can be found at the end of this page, under the Links section. The following sections explain how to use them in your own code. Attribute selection - removing irrelevant attributes from your data.Evaluating - how good is the classifier/clusterer?.Classifier/Clusterer - built on the processed data.Start by opening data Iris data in dataset Set, namely iris.The most common components you might want to use are After equal frequency discretization RI attribute, Here's the picture : Leave the default parameters unchanged, spotīlow Apply, As shown in the figure below :Įqual frequency discretization : Set up Discretize Medium The value is true. Stay data Glass dataset found in directory glass.arff fileĮqual width discretization : In turn, open choose-weka-filters-unsupervised-attribute-Discretize. The filter deletes instances of a given range in the dataset, Click on Choose Next to the text box , choice choose-weka-filter-unsupervised-instance-RemoveRange, The filter deletes a given percentage of instances in the dataset, Click on Choose Next to the text box ,Īpply After that, there was only one left 1 Data Click on Choose Next to the text box, The following dialog box will pop upĬlick on apply There are only two pieces of data leftĬhoose-weka-filter-unsupervised-instance-RemovePercentage, choice choose-weka-filter-unsupervised-instance-RemoveFolds, too filter The data set is divided into a given number of cross validation folds by the, And specify the number of output folds. Still click Choose Button, successively weka-filter- Unsupervised -attribute-AddUserFileds filter The filter suitable for deleting attributes is Remove, We're under no supervision \attribute Find below Remove entry Īfter loading the data, the following page appears Utilize weka The preprocessing algorithm in preprocesses the data, Include : Add attribute, Delete attribute / example, Discretize the data. The prompt of successful database connection appears Open after setting weka, Go to the Explorer page Third, Modify the following configuration file Second, Start database, Set up the name weka The database of, And establish the following table %WEKA_HOME%\lib\mysql-connector-java-5.1.47.jar When mining data in the database, We need to weka And mysql Connectįirst of all, Configure environment variables Utilize weka Realize the data mining in the database See data Folder, Inside is weka Native data set Steps and results analysis weka Self contained test data set utilize weka The preprocessing algorithm in preprocesses the data, Include : Add attribute, Delete attribute / example, Discretize the data. utilize weka Realize the data mining in the database ģ. analysis weka Self contained test data set Ģ. 「 This is my participation 11 The fourth of the yuegengwen challenge 10 God, Check out the activity details : 2021 One last more challenge 」 Contentġ.