文件名称:OPTICS-algorithm---Wikipedia--the-free-encycloped
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OPTICS ("Ordering Points To Identify the Clustering Structure") is an algorithm for finding density-based clusters in spatial data. It was
presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö rg Sander[1]. Its basic idea is similar to DBSCAN,[2] but it
addresses one of DBSCAN s major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the
points of the database are (linearly) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a
special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to
the same cluster. This is represented as a dendrogram.
presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jö rg Sander[1]. Its basic idea is similar to DBSCAN,[2] but it
addresses one of DBSCAN s major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the
points of the database are (linearly) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a
special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to
the same cluster. This is represented as a dendrogram.
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| 文件名 | 大小 | 更新时间 |
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| OPTICS algorithm - Wikipedia | the free encyclopedia.pdf |
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