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OPTICS (3.6)

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.

Options

Class column

Choose the column that contains the target variable.

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

Clusterer 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.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: .)

Input Ports

: Training data

Output Ports

: Trained clusterer

Popular Predecessors

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Popular Successors

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Views

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.

Workflows

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Developers

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Installation

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:

v5.1

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 2.10.2.v202301130909

On NodePit since: 2023-07-15

Last update: 2024-10-30

KNIME versions: From v3.6 to v5.1

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