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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク OPTICS algorithm ： ウィキペディア英語版 OPTICS algorithm Ordering points to identify the clustering structure (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented 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. 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. ==Basic idea== Like DBSCAN, OPTICS requires two parameters: $\backslash varepsilon$, which describes the maximum distance (radius) to consider, and $MinPts$, describing the number of points required to form a cluster. A point $p$ is a ''core point'' if at least $MinPts$ points are found within its $\backslash varepsilon$-neighborhood $N\_\backslash varepsilon(p)$. Contrary to DBSCAN, OPTICS also considers points that are part of a more densely packed cluster, so each point is assigned a ''core distance'' that describes the distance to the $MinPts$th closest point: : The ''reachability-distance'' of another point $o$ from a point $p$ is the distance between $o$ and $p$, or the core distance of $p$: : If $p$ and $o$ are nearest neighbors, this is the we need to assume in order to have $p$ and $o$ belong to the same cluster. Both the core-distance and the reachability-distance are undefined if no sufficiently dense cluster (w.r.t. $\backslash varepsilon$) is available. Given a sufficiently large $\backslash varepsilon$, this will never happen, but then every $\backslash varepsilon$-neighborhood query will return the entire database, resulting in $O(n^2)$ runtime. Hence, the $\backslash varepsilon$ parameter is required to cut off the density of clusters that is no longer considered to be interesting and to speed up the algorithm this way. The parameter $\backslash varepsilon$ is, strictly speaking, not necessary. It can simply be set to the maximum possible value. When a spatial index is available, however, it does play a practical role with regards to complexity. It is often claimed that OPTICS abstracts from DBSCAN by removing this parameter, at least to the extent of only having to give the maximum value. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「OPTICS algorithm」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.