OPTICS algorithm
Algorithm for finding density based clusters in spatial data
Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented in 1999 by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander.
Nº Q2007847 ★
Common · Knowledge
OPTICS algorithm
Algorithm for finding density based clusters in spatial data
Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented in 1999 by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander.
Last price
—
Floor price
—
7-day median
—
30-day sales
0
30-day range
—
In circulation
0
Price history
median
low – high
sales
No sales in this period
Show table
| Date | median | Low | High | sales |
|---|
Sales history
- Last sale
- —
- 30-day average
- —
- 30-day low
- —
- 30-day high
- —
- Sales 7d
- 0
- Sales 30d
- 0
No sales yet.
Anonymous sales: no buyer or seller shown. Figures count player-to-player sales only.
From Wikipedia
Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented in 1999 by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. Its basic idea is similar to DBSCAN, but it addresses one of DBSCAN's major weaknesses: the problem of detecting meaningful clusters in data of varying density. To do so, the points of the database are (linearly) ordered such that spatially closest points become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that must be accepted for a cluster so that both points belong to the same cluster. This is represented as a dendrogram.
Text: Wikipédia, CC BY-SA 4.0. · Image: Chire (Public domain) ·
Related cards
-
DBSCAN
Data clustering algorithm
Nº Q1114630 ★★
Not listed
-
Optics
Branch of physics concerning light
Nº Q14620 ★★★
Not listed
-
K-means clustering
Vector quantization algorithm that minimizes the sum of squared deviations between points and their nearest mean
Nº Q310401 ★★★
Not listed
-
A
Apdex
Organization
Nº Q4779265 ★
Not listed
-
Cluster analysis
Task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters)
Nº Q622825 ★★
Not listed
-
Ant colony optimization algorithms
Probabilistic techniques for solving computational problems that can be reduced to finding good paths through graphs
Nº Q460851 ★★
Not listed