New release: Rapid-I released a new version of the leading open-source data mining solution RapidMiner 4.2

RapidMiner 4.2: New Small Enterprise Edition

The new version of course is again available in different versions. Users can choose between the free Community Edition of RapidMiner and one of the Enterprise Editions which are more suitable for professional data analysts:

      Enterprise Edition = 
            Community Edition +
            More Features +
            Services +
            Guarantees

The good news for smaller companies or single users is that we now also provide the new Small Enterprise Edition. Check out the details at the Enterprise Edition feature page!

RapidMiner 4.2: Focus on Large-Scale Data Mining

This release is a minor bugfix and major feature release. The access to databases is improved and provides now constant access times for databases with arbitrary sizes. The focus on large-scale data mining is also demonstrated by the re-implementations of several learning schemes leading to a boost in running time of up to an factor of 13. Several unnecessary data scans were removed leading to further reduced runtimes.

General Improvements:

• Aggregation now supports multiple grouping and multiple aggregation attributes with different aggregation functions each
• Date type now better supported including several powerful conversion operators for almost arbitrary date and time formats
• Histogram plotters now support jittering and log scales
• Improved database wizard now supporting larger data sets which caused memory problems in the older versions during table and attribute name retrieval
• Statistics for larger data sets are now only calculated on request
• Reduced the iteration time through partitioned / splitted data sets
• All plotters can now handle missing values
• Many plotters now support the plotting of absolute values and / or sorting according to the plotted column
• One-Class SVM for LibSVMLearner now properly supported
• New operators GroupModel and UngroupModel now replace the automatic building of ContainerModels (merging preprocessing with prediction models) and hence give the user more control over the model building / grouping process
• AttributeSubsetPreprocessing now supports the inversion of the specified regular expression
• AttributeSubsetPreprocessing can now be applied on subsets defined similarly to the new AttributeFilter operator. Hence, the subset preprocessing can for example only be performed on nominal or numerical attributes only
• The database example set writer now supports new overwriting / appending modes
• and a lot more

New Operators: More than 10 new operators were added since version 4.1: Nominal2Date Date2Nominal KernelPCA EqualLabelWeighting StataExampleSource FeatureSubsetIteration RelativeRegression AttributeValueSubstring CachedDatabaseExampleSource NameBasedWeighting BatchProcessing GroupModel UngroupModel

Bugfixes:

Links