OPTICS (3.7)

Basic implementation of OPTICS clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations exist! Clustering of new instances is not supported

More info:

Mihael Ankerst, Markus M.Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure.

In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


OPTICS Options

E: epsilon (default = 0.9)

M: minPoints (default = 6)

I: index (database) used for OPTICS (default = weka.clusterers.forOPTICSAndDBScan.Databases.SequentialDatabase)

D: distance-type (default = weka.clusterers.forOPTICSAndDBScan.DataObjects.EuclideanDataObject)

F: write results to OPTICS_#TimeStamp#.TXT - File

no-gui: suppress the display of the GUI after building the clusterer

db-output: The file to save the generated database to. If a directory is provided, the database doesn't get saved. The generated file can be viewed with the OPTICS Visualizer: java weka.clusterers.forOPTICSAndDBScan.OPTICS_GUI.OPTICS_Visualizer [file.ser] (default: .)

Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, No class] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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