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**KNIME JavaScript Views (Labs)** version **4.4.0.v202104131324** by **KNIME AG, Zurich, Switzerland**

The implementation of the OPTICS algorithm in KNIME consists of two nodes: the OPTICS Cluster Compute node and the OPTICS Cluster Assigner node. Here you will find a brief description of the algorithm, followed by a description of the OPTICS Cluster Assigner node.

OPTICS is an algorithm for finding clusters in spatial data. It was
first described in Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander (1999). "OPTICS: Ordering Points To Identify the Clustering Structure".
ACM SIGMOD international conference on Management of data. ACM Press. pp. 49–60 and is based on DBSCAN. Like DBSCAN,
it is a *density-based* clustering algorithm, that is, it groups data points that are densely packed together in some
contiguous region of the data space into clusters. Unlike DBSCAN, it can detect
clusters of varying density.

The basic idea shared by DBSCAN and OPTICS is that a data point *p*
belongs to a cluster if it has sufficiently many sufficiently close
neighbors. "Sufficiently close" is modeled with a parameter **epsilon** (the
neighborhood radius of *p*), "sufficiently many" with a parameter **Minimum
Points** (the minimum number of points that must be within **epsilon** of *p* in
order for *p* to belong to the core of a cluster.)

DBSCAN first checks the epsilon-neighborhood of each point in the data
set. If there are more than **Minimum Points** points in the
epsilon-neighborhood of a point *p*, these points form a cluster. DBSCAN
then checks the epsilon-neighborhoods of these points to see if they in
turn contain more than **Minimum Points** data points. If they do, these
data points also become part of the cluster. DBSCAN continues in this
way until no new point can be added to the cluster.

While this algorithm will find all the clusters with the density
determined by the the chosen values for **epsilon** and **Minimum Points**, it
may miss higher-density clusters that are contained in these
clusters. These higher-density clusters become visible only at some
**epsilon-prime** <e epsilon. The problem is that there is no way of knowing these
**epsilon-prime** values in advance, so all one can do is run DBSCAN for as many
**epsilon-prime** values as feasible.

OPTICS solves this problem by ordering the points in the data set and by
associating with each point two values: its core-distance and its
reachability distance (for definitions, see the link above). This
information is enough to find all density-based clusters in the data set
for any **epsilon-prime** <e **epsilon** (for details on the algorithm, see the link
above).

Since both core-distance and reachability-distance are ultimately
defined in terms of **epsilon** and **Minimum Points**, the only inputs needed
to run OPTICS are values for these parameters.

The OPTICS Cluster Assigner node takes the linear ordering produced by
the OPTICS Cluster Compute node as input and assigns each point in the
ordering to a cluster. The assignment depends on the value of
**epsilon-prime**, which can be set in the Dialog Options.

The node supports custom CSS styling. You can simply put CSS rules into a single string and set it as a flow variable 'customCSS' in the node configuration dialog. You will find the list of available classes and their description on our documentation page.

The first tab allows you to set the value of **epsilon-prime** and the number of bins.

- Epsilon-Prime
- You can set the value of
**epsilon-prime**equal to the mean or to the median of the reachability distances, or you can set it manually. If you change the value of**epsilon-prime**in interactive view and save it, the dialog window will show "Manual input" and the saved value. - Number of bins
- The number of bins determines the visual representation of data points. The greater the number of bins is, the more fine-grained is the visual representation of the data points.

- Chart title (*)
- The title shown above the image. If left blank, no title will be shown.
- Chart subtitle (*)
- The subtitle shown above the image. If left blank, no subtitle will be shown.
- Show warnings in view
- Checking this option enables the display of warning messages.
- Width of image (in px)
- The width in pixels of the generated SVG image.
- Height of image (in px)
- The height in pixels of the generated SVG image.
- Resize view to fill window
- Checking this option resizes the view so that it fills the window.
- Display full screen button
- Displays a button enabling full screen mode.
- Create image at outport
- Check this option if you want an image to be created at the upper outport.
- Enable view edit controls
- If checked, all edit controls selected below will be rendered in the view. Unchecking this option disables all edit controls.
- Enable title edit controls
- Displays a text box to change title or subtitle.
- Enable epsilon prime edit controls
- Displays a text box to change the
**epsilon-prime**value. - Enable selection
- When this option is checked, data points can be selected in the scatter plot by activating the corresponding button and clicking on points. Extend the selection by holding down the Shift-key while clicking on points. The selection appears in an appended column of the data table.
- Publish selection events
- Checking this option ensures that other views are notified whenever the selection in the current view is changed. See also "Subscribe to selection events".

<p>Clicking on "Interactive View: Reachability Distance Plot" in the context menu brings up a bar chart representing data points, with the height of the bars representing reachability distance. What clusters you see depends on the location of the <b>epsilon-prime</b> line. The higher you drag it, the fewer clusters you see.</p>

- Image port.
- The input data with a column detailing each tuple's Cluster ID.
- Summary table with counts for each cluster.

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To use this node in KNIME, install KNIME JavaScript Views (Labs) from the following update site:

KNIME 4.4

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