翻訳と辞書 Words near each other ・ Subclass reachability ・ Subclavian ・ Subclavian artery ・ Subclavian groove ・ Subclavian loop ・ Subclavian lymph trunk ・ Subclavian nerve ・ Subclavian steal syndrome ・ Subclavian triangle ・ Subclavian vein ・ Subclavius muscle ・ Subclemensia ・ Subclinical infection ・ Subclinical seizure ・ Subcloning ・ SUBCLU ・ Subclub ・ Subclytia ・ Subclytia rotundiventris ・ Subcoal ・ Subcoccinella vigintiquatuorpunctata ・ Subcollina ・ Subcoloring ・ Subcomandante Elisa ・ Subcomandante Marcos ・ Subcomandante Marcos bibliography ・ Subcommander ・ Subcommentaries, Theravada ・ Subcommissural organ ・ Subcommittee (Cragg) Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク SUBCLU ： ウィキペディア英語版 SUBCLU SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger.〔Karin Kailing, Hans-Peter Kriegel and Peer Kröger. ''Density-Connected Subspace Clustering for High-Dimensional Data''. In: ''Proc. SIAM Int. Conf. on Data Mining (SDM'04)'', pp. 246-257, 2004.〕 It is a subspace clustering algorithm that builds on the density-based clustering algorithm DBSCAN. SUBCLU can find clusters in axis-parallel subspaces, and uses a bottom-up, greedy strategy to remain efficient. ==Approach== SUBCLU uses a monotonicity criteria: if a cluster is found in a subspace $S$, then each subspace $T\; \backslash subseteq\; S$ also contains a cluster. However, a cluster $C\; \backslash subseteq\; DB$ in subspace $S$ is not necessarily a cluster in $T\; \backslash subseteq\; S$, since clusters are required to be maximal, and more objects might be contained in the cluster in $T$ that contains $C$. However, a density-connected set in a subspace $S$ is also a density-connected set in $T\; \backslash subseteq\; S$. This ''downward-closure property'' is utilized by SUBCLU in a way similar to the Apriori algorithm: first, all 1-dimensional subspaces are clustered. All clusters in a higher-dimensional subspace will be subsets of the clusters detected in this first clustering. SUBCLU hence recursively produces $k+1$-dimensional candidate subspaces by combining $k$-dimensional subspaces with clusters sharing $k-1$ attributes. After pruning irrelevant candidates, DBSCAN is applied to the candidate subspace to find out if it still contains clusters. If it does, the candidate subspace is used for the next combination of subspaces. In order to improve the runtime of DBSCAN, only the points known to belong to clusters in one $k$-dimensional subspace (which is chosen to contain as little clusters as possible) are considered. Due to the downward-closure property, other point cannot be part of a $k+1$-dimensional cluster anyway. 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「SUBCLU」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.