多頻度グラフパターンの完全な高速マイニング手法

概要

論文の詳細を見る
In the field of data mining, much attention has been paid to the Basket Analysis. Basket Analysis derives frequent itemsets from database, but its ability of mining is limited to transaction data consisting of items. Some approaches to discover characteristic graph structure from a set of given graph structured data have been researched in the field of inductive machine learning. However, most of them are not suitable for the applications which require to search all frequent graph patterns in the large amount of data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in massive graph structured data. Our algorithm can derive all frequent patterns from both the directed and undirected graph structured data having loops, multiple types of nodes and links.
社団法人人工知能学会の論文
2000-11-01