* Parameters will now be adapted according to an operator rename, for example the settings of operators like the ProcessLog or the parameter optimization operators are automatically corrected to the new operator names
* Graphs like the similarity graph display the strengths of the edges now by their color
* Added new tree layout algorithm for the decision trees preventing most overlapping, the old tighter version is available as layout type "Tree (Tight)"
* Decision trees now show the subtree size as tool tip for the inner nodes, the edges are now darker for larger subtrees and brighter for smaller ones
* Tables like the (meta) data view now supports a new context menu for common table operations like column sorting or row / column selection
* The New Operator dialog now also supports full text search in the description texts of the operators
* RapidMiner now stores all parameter values in the process files including the default values which ensures a better compatibility with future versions. The XML tab, however, only shows the values differing from the default
* Univariate and multivariate series windowing operators now also support nominal attributes and even mixed types in cases where the series is represented by the examples (rows) of the data set
* The range statistics of nominal attributes in the meta data view now shows the values with highest and lowest occurrency counts, sorts the values according to the counts, and displays only an excerpt of the occurring values if large amounts of different values exist
* List of recent files is now directly saved after opening a new process and not only during shutdown
* Changes in the process setup are now allowed even during process runtime, e.g. when waiting at a breakpoint
* Updated to latest version of Weka (as of February 26th, 2009)
* Bugfixes:
- fixed bug accuracy criterion for the revised decision tree learner - Fixed bug in parameter list of ValueSubgroupIterator - Fixed bug in ExceptionHandling which sometimes led to doubled outputs - Fixed bug in ProcessBranch which sometimes led to doubled outputs - ViewAttributes did not add min and max statistics so that those statistics where not calculated on data table views
Changes from RapidMiner 4.3.1 to RapidMiner 4.3.2 [2009/02/17] ---------------------------------------------------------------
- ExampleSetWriter: quote_whitespace is now named quote_nominal_values
* ExampleSetMerge can now handle missing values
* RapidMiner does now better support counts for the in- and output types which should considerably reduce the amount of warnings if operators like IOConsumer, IOMultiplier or ExampleSetMerge (reducing several objects of the same type to one of the same) are used
* FileIterator replaces DirectoryIterator and adds many new features like recursive iteration, file name based filtering, and a new macro for the parent path
* Centroid based clusterings now support assigning unseen examples to the nearest cluster on apply time
* ProcessBranch now supports a branching with respect to the existance of an input object
* ClearProcessLog now also allows to remove the complete logging table
* The logging tables of the ProcessLog operator will now not be generated during start up but during the first operator usage (and also during the following if the table was deleted in the meantime, e.g. in a loop)
* Added support for different time zones, users can now define the preferred time zone in the settings dialog and time conversion operators are not able to respect this setting
* Date and times are now displayed in the system's local settings
* New plotter: Block
* Added support for applying a log scale for the color column for the Scatter plot and the new Block plotter
* Data tables like those generated by the process log are now de-coupled from the table used for plotting preventing that the rows will be sampled and rows would be removed from the data table
* A double click on the region between two columns in the table header now automatically resizes the left column to a fitting size (known from Windows programs)
* A double click on the same region while pressing CTRL will resize all table columns according to the contents
* GuessValueTypes now only works on regular attributes and provides a parameter for extending it on the special attributes (work_on_special)
* AttributeFilter now also provides a new parameter work_on_special
* The operator Replace now also allows empty replace_by values
* The ExampleSetJoin operator now also works if the id of the first example set is not part of the second
* Guess value types can now handle missing values
* CSVExampleSetWriter now supports the parameter quote_nominal
* All feature selection and weighting operators now also provide the possibility to log the names of the features of the current generation's best individual
* The Replace operator now supports capturing groups
* The file based example source operators (ExampleSource, SimpleExampleSource, CSVExampleSource...) now better supports quoted strings and also escaped quotes (escaping with \")
* Implementation details:
- The method Tools.quotedSplit(...) should now be used instead of a regular split followed by the method Tools.mergeQuotedSplits(...)
* Bugfixes:
- fixed bug in DBScan for empty cluster models ——固定错误简单抽样的情况下local random seed was used - fixed bug in process logging to files which prevented the writing of the first logged result - fixed bug in PSO optimization for cases where the fitness should be minimized instead of maximized - fixed bug in binary performance measure which was not delivering the fitness for specificity, sensitivity, and youden index - fixed bug in meta data table viewer in cases where huge numbers of long nominal values existed which caused a crash of the Java Virtual Machine in some cases
Changes from RapidMiner 4.3 to RapidMiner 4.3.1 [2009/01/12] ---------------------------------------------------------------
* Revised decision tree learning which lead to drastically reduced runtimes and better tree models in terms of generalization capabilities
* The bar chart now displays the category as label in the domain axis
* Removed plotter: Bars 3D
* The IOObjectReader now allows the definition of the expected output type
* The LiftParetoChart does no longer re-apply the input model if a predicted label does already exist
* Added the ability to "explode" tiles of pie and ring charts
* Added several new options for the reporting operators of the RapidMiner Enterprise Edition as well as true parameter handling including type checks
* Updated to latest release of Jung
* Fixed GUI related memory leaks
* Implementation details:
- The class AttributeWeightsCreator was renamed to ExampleSet2AttributeWeights
* Bugfixes:
- Fixed a combination of GUI and process thread related memory leaks - Fixed bug in Series Multiple Plotter which prevented rescaling - Pie and Bar charts used class limit instead of legend 限制为了决定是否应该年代传奇hown - special format in ExampleSetWriter ignored quote whitespace setting - bug in XVPrediction fixed
Hi Ema, Cluster2Prediction enables you to use classification performance measures for clustering, if label informations are available. For example think of the situation, where you know what has to be in the same cluster for a subset of your data. You then might use any flat clustering algorithm and test if it discovers your cluster structure. To achieve this, the operator matches the given cluster labels with the class labels in the best fitting way and converts the clusterattribute into a prediction attribute. You then might use the standard performance operators for classification to calculate the performance.
Guru
Answers
here's a snapshot from the changes.txt in the current developer repository:
Changes from RapidMiner 4.3.2 to RapidMiner 4.4 [2009/??/??]
---------------------------------------------------------------
* New operators:
- ExampleSetSuperset
- ExampleSetUnion
- MacroConstruction
- CumulateSeries
- FastLargeMargin
* Parameters will now be adapted according to an operator
rename, for example the settings of operators like
the ProcessLog or the parameter optimization operators
are automatically corrected to the new operator names
* Graphs like the similarity graph display the strengths
of the edges now by their color
* Added new tree layout algorithm for the decision trees
preventing most overlapping, the old tighter version
is available as layout type "Tree (Tight)"
* Decision trees now show the subtree size as tool tip
for the inner nodes, the edges are now darker for
larger subtrees and brighter for smaller ones
* Tables like the (meta) data view now supports a new
context menu for common table operations like column
sorting or row / column selection
* The New Operator dialog now also supports full text
search in the description texts of the operators
* RapidMiner now stores all parameter values in the
process files including the default values which ensures
a better compatibility with future versions. The XML tab,
however, only shows the values differing from the default
* Univariate and multivariate series windowing operators
now also support nominal attributes and even mixed
types in cases where the series is represented by
the examples (rows) of the data set
* The range statistics of nominal attributes in the
meta data view now shows the values with highest and
lowest occurrency counts, sorts the values according
to the counts, and displays only an excerpt of the
occurring values if large amounts of different values
exist
* List of recent files is now directly saved after opening
a new process and not only during shutdown
* Changes in the process setup are now allowed even during
process runtime, e.g. when waiting at a breakpoint
* Updated to latest version of Weka (as of February 26th, 2009)
* Bugfixes:
- fixed bug accuracy criterion for the revised decision
tree learner
- Fixed bug in parameter list of ValueSubgroupIterator
- Fixed bug in ExceptionHandling which sometimes led to
doubled outputs
- Fixed bug in ProcessBranch which sometimes led to
doubled outputs
- ViewAttributes did not add min and max statistics
so that those statistics where not calculated on
data table views
Changes from RapidMiner 4.3.1 to RapidMiner 4.3.2 [2009/02/17]
---------------------------------------------------------------
* New operators:
- LinearDiscriminantAnalysis
- QuadraticDiscriminantAnalysis
- RegularizedDiscriminantAnalysis
- DasyLabExampleSource
- FileIterator
- ExceptionHandling
- ChangeAttributeNamesReplace
- ChangeAttributeNames2Generic
- DateAdjust
- MinMaxBinDiscretization
- RainflowMatrix
* Deprecated operators:
- DirectoryIterator (use FileIterator instead)
* Renamed parameters:
- ExampleSetWriter:
quote_whitespace is now named quote_nominal_values
* ExampleSetMerge can now handle missing values
* RapidMiner does now better support counts for the in-
and output types which should considerably reduce the
amount of warnings if operators like IOConsumer,
IOMultiplier or ExampleSetMerge (reducing several objects
of the same type to one of the same) are used
* FileIterator replaces DirectoryIterator and adds many
new features like recursive iteration, file name based
filtering, and a new macro for the parent path
* Centroid based clusterings now support assigning unseen
examples to the nearest cluster on apply time
* ProcessBranch now supports a branching with respect
to the existance of an input object
* ClearProcessLog now also allows to remove the complete
logging table
* The logging tables of the ProcessLog operator will now
not be generated during start up but during the first
operator usage (and also during the following if the
table was deleted in the meantime, e.g. in a loop)
* Added support for different time zones, users can now
define the preferred time zone in the settings dialog
and time conversion operators are not able to respect
this setting
* Date and times are now displayed in the system's local
settings
* New plotter: Block
* Added support for applying a log scale for the color
column for the Scatter plot and the new Block plotter
* Data tables like those generated by the process log
are now de-coupled from the table used for plotting
preventing that the rows will be sampled and rows
would be removed from the data table
* A double click on the region between two columns in
the table header now automatically resizes the left
column to a fitting size (known from Windows programs)
* A double click on the same region while pressing CTRL
will resize all table columns according to the contents
* GuessValueTypes now only works on regular attributes
and provides a parameter for extending it on the special
attributes (work_on_special)
* AttributeFilter now also provides a new parameter
work_on_special
* The operator Replace now also allows empty replace_by
values
* The ExampleSetJoin operator now also works if the
id of the first example set is not part of the second
* Guess value types can now handle missing values
* CSVExampleSetWriter now supports the parameter quote_nominal
* All feature selection and weighting operators now also
provide the possibility to log the names of the features
of the current generation's best individual
* The Replace operator now supports capturing groups
* The file based example source operators (ExampleSource,
SimpleExampleSource, CSVExampleSource...) now better
supports quoted strings and also escaped quotes (escaping
with \")
* Implementation details:
- The method Tools.quotedSplit(...) should now be used
instead of a regular split followed by the method
Tools.mergeQuotedSplits(...)
* Bugfixes:
- fixed bug in DBScan for empty cluster models
——固定错误简单抽样的情况下local
random seed was used
- fixed bug in process logging to files which prevented
the writing of the first logged result
- fixed bug in PSO optimization for cases where the fitness
should be minimized instead of maximized
- fixed bug in binary performance measure which was not
delivering the fitness for specificity, sensitivity,
and youden index
- fixed bug in meta data table viewer in cases where huge
numbers of long nominal values existed which caused a
crash of the Java Virtual Machine in some cases
Changes from RapidMiner 4.3 to RapidMiner 4.3.1 [2009/01/12]
---------------------------------------------------------------
* New operators:
- RemoveDuplicates
- Cluster2Prediction
- DirectoryIterator
- TextObjectWriter
- TextObjectLoader
- TextExtractor
- SingleTextObjectInput
- TextCleaner
- TextObject2ExampleSet
- TextSegmenter
- AddAttribute
- SetData
- EMClustering
- AttributeWeights2ExampleSet
- TransitionGraph
- DatabaseExampleVisualizationOperator
* Revised decision tree learning which lead to drastically
reduced runtimes and better tree models in terms of
generalization capabilities
* The bar chart now displays the category as label in the
domain axis
* Removed plotter: Bars 3D
* The IOObjectReader now allows the definition of the expected
output type
* The LiftParetoChart does no longer re-apply the input model if
a predicted label does already exist
* Added the ability to "explode" tiles of pie and ring charts
* Added several new options for the reporting operators of the
RapidMiner Enterprise Edition as well as true parameter handling
including type checks
* Updated to latest release of Jung
* Fixed GUI related memory leaks
* Implementation details:
- The class AttributeWeightsCreator was renamed to
ExampleSet2AttributeWeights
* Bugfixes:
- Fixed a combination of GUI and process thread related
memory leaks
- Fixed bug in Series Multiple Plotter which prevented
rescaling
- Pie and Bar charts used class limit instead of legend
限制为了决定是否应该年代传奇hown
- special format in ExampleSetWriter ignored quote
whitespace setting
- bug in XVPrediction fixed
Hope that satisfies your needs :P
Greetings,
Sebastian
then you could check out the developer version using the developer branch from cvs? A guide for checking out using eclipse is on our website.
Greetings,
Sebastian
Cheers,
Ingo
downloaded the new Rapidminer...
我在想如何use the Cluster2Prediction ?
Thank you
Cluster2Prediction enables you to use classification performance measures for clustering, if label informations are available. For example think of the situation, where you know what has to be in the same cluster for a subset of your data. You then might use any flat clustering algorithm and test if it discovers your cluster structure. To achieve this, the operator matches the given cluster labels with the class labels in the best fitting way and converts the clusterattribute into a prediction attribute. You then might use the standard performance operators for classification to calculate the performance.
Greetings,
Sebastian
Thank you very much
It works great
but with aggolom_clustering i tried to use it but
it is not working
i tried to flattern then to use example2cluster
but still can not work ...
Thank you in advance
there seems to be a problem during the flattening of the agglomerative clustering. I send this topic to Sebastian who is our clustering expert.
Cheers,
Ingo