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 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify clusterer-specific parameters.
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
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.EuclidianDataObject)
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: .)
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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