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**KNIME JavaScript Views (Labs)** version **4.3.0.v202011191515** 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 Compute 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.

- Distance Selection
- Optional. When the distance port is not connected, select the distance metric to be used.
- Select Column
- Optional. When the distance port is not connected, select the columns for which distance is to be calculated. Columns can be selected manually or by means of regular expressions. Columns listed in the left (red) pane will be excluded, columns in the right (green) pane will be included. Use the buttons in the center to move columns from one pane to the other.
- Minimum points
- The minimum number of points within
**epsilon**of a point*p*in order for*p*to belong to the core of a cluster. - Epsilon
- The neighborhood radius of a point
*p*. Points within this distance of each other are considered neighbors.

- Numeric Distances (25 %)
- Column Filter (10 %) Streamable
- Normalizer (7 %)
- Distance Matrix Calculate (7 %)
- Parameter Optimization Loop Start (4 %)
- Show all 53 recommendations

- OPTICS Cluster Assigner (99 %)
- Cluster Assigner (< 1 %) Streamable

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

KNIME 4.3

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

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