翻訳と辞書 Words near each other ・ Cooperative Patent Classification ・ Cooperative Polygraphy ・ Cooperative principle ・ Cooperative Research and Development Agreement ・ Cooperative Research Centre ・ Cooperative Research Centre for Advanced Composite Structures ・ Cooperative Security Location ・ Cooperative State Research, Education, and Extension Service ・ Cooperative storage cloud ・ Cooperative sugar factories and rural development ・ Cooperative synapse formation ・ Cooperative University of Colombia ・ Cooperative Village ・ Cooperative web ・ Cooperative Wheat Pool of Western Australia ・ Cooperative-based graph clustering ・ Cooperativeness ・ Cooperatives Europe ・ Cooperatives of Norway ・ Cooperatives Secretariat ・ Cooperativity ・ Cooperator ・ Cooperconcha ・ Cooperconcha centralis ・ Cooperfiel ・ Cooperia ・ Cooperite ・ Cooperl Arc Atlantique ・ Cooperman ・ Coopernail sign 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 Cooperative-based graph clustering ： ウィキペディア英語版 Cooperative-based graph clustering "Cooperative Clustering on Graphs" (CC/G) is a graph-based clustering approach that combines multiple state-of-the-art software clustering algorithms with a cooperative strategy to produce a measurably better and more stable results than any of those algorithms does individually. CC/G is useful for addressing many graph-mining research challenges. It can identify anomalies in graph-based datasets, which is applicable in fields including but not limited to network intrusion, fraud detection, health monitoring, and sensor networks. It can also be used as a technique for subgraph matching in the context of dynamic graphs and datasets that represent the temporal evolution of relationships between entities. ==Description== Cooperative Clustering on Graphs is an unsupervised learning algorithm for clustering a graph networks into k partitions based on intra-cluster density and inter-cluster sparsity. The main idea is to apply different clustering algorithms on the node-edge network. Each algorithm will provide different k clusters. A common agreement among those clusterings is then found. This agreement identifies the minimum number of k clusters that the graph network should have. The last step is to merge the remaining graph elements that exhibited disagreement in-between the clusters that were initially determined using optimum intra-cluster density and inter-cluster sparsity. These steps are illustrated by the following figure. The algorithm makes no assumptions about the size or the number of clusters. Besides this, the algorithm can make use of multiple clustering criteria functions. Also, It overcomes the drawbacks of clustering algorithms that the result will not vary greatly unless the data change in the same amount when using different clustering criteria. 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「Cooperative-based graph clustering」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.