OPTICS
OPTICS (Ordering Points To Identify the Clustering Structure) je algoritam za grupisanje zasnovan na gustini koji su uveli Ankerst, Breunig, Kriegel i Sander 1999. godine. On generalizuje DBSCAN obradom tačaka u redosledu koji kodira punu strukturu grupisanja zasnovanu na gustini skupa podataka, omogućavajući detekciju grupa različitih gustina putem dijagrama dostižnosti (reachability plot) umesto zahtevanja fiksiranog globalnog praga gustine.
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Method map
The neighbourhood of related methods — select a node to explore.
Izvori
- Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60. DOI: 10.1145/304181.304187 ↗
- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. link ↗
- Aggarwal, C. C., & Reddy, C. K. (Eds.) (2013). Data Clustering: Algorithms and Applications (Ch. 4). CRC Press. ISBN: 978-1-4665-5821-2
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). OPTICS: Ordering Points To Identify the Clustering Structure. ScholarGate. https://scholargate.app/sr/machine-learning/optics
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- DBSCANMašinsko učenje↔ compare
- HDBSCANMašinsko učenje↔ compare
- Hijerarhijsko grupisanjeMašinsko učenje↔ compare
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