Live briefing
Everything about 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. 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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: OPTICS algorithmA 5-minute orientation
- Skim a recent paper: Application of AdamSPGD algorithm to sensor-less adaptive optics in coherent free-space optical communication systemInfluential work since 2022
- Pick up a classic book: Pattern RecognitionThe most-reprinted book on the subject
- Read the landmark paper: Genetic algorithm assisted meta-atom design for high-performance metasurface opticsCited 144 times
- Go sideways: DBSCANA closely related topic
- Go sideways: Local outlier factorA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Genetic algorithm assisted meta-atom design for high-performance metasurface optics
Implementation of Quantum Search Algorithm using Classical Fourier Optics
Dwell-time algorithm for polishing large optics
Complex Inverse Design of Meta-optics by Segmented Hierarchical Evolutionary Algorithm
A fast physical optics (FPO) algorithm for high frequency scattering
A fast physical optics (FPO) algorithm for double-bounce scattering
Wave Optics Optimizer: A novel meta-heuristic algorithm for engineering optimization
Multilevel fast physical optics algorithm for radiation from non-planar apertures
🆕 Influential since 2022
Application of AdamSPGD algorithm to sensor-less adaptive optics in coherent free-space optical communication system
An independent central point OPTICS clustering algorithm for semi-supervised outlier detection of continuous glucose measurements
Design and Performance Analysis of NadamSPGD Algorithm for Sensor-Less Adaptive Optics in Coherent FSOC Systems
Adaptive OPTICS Algorithm Denoising ICESat-2 Laser Photon Data
A Novel SPGD Algorithm for Wavefront Sensorless Adaptive Optics System
📚 Books worth reading
Pattern RecognitionCheng-Lin Liu · 2012
Image Analysis and RecognitionAurélio Campilho · 2004
Artificial general intelligenceBen Goertzel, Cassio Pennachin · 2007
Membrane ComputingMarian Gheorghe, Grzegorz Rozenberg · 2012
Machine Learning and Data Mining in Pattern RecognitionPetra Perner, Atsushi Imiya · 2003
Artificial EvolutionEvelyne Lutton, Pierrick Legrand · 2008- 📕Mathematical OpticsVasudevan Lakshminarayanan, Maria Luisa Calvo · 2012
- 📕A model-based approach for the measurement of eye movements using image processingKwangjae Sung · 1997
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- DBSCAN Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg…
- Local outlier factor In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in…
- ELKI ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is a data mining (KDD, knowledge discovery in databases) software framework…
- Cluster analysis Cluster analysis, or clustering, is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group…
- SUBCLU SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger. It is a subspace clustering algorithm…
- Arthur Zimek Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark. He graduated from…
- Clustering high-dimensional data Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional…
- Density-based clustering validation Density-Based Clustering Validation (DBCV) is a metric designed to assess the quality of clustering solutions, particularly for density-based clustering…