这次简单分析一下模式挖掘中的图模式挖掘方向,包括研究什么内容以及对应的研究方法。 模式挖掘研究的一般是某个数据库中频繁出现的子模式,那么图模式挖掘的研究内容自然是在图数据库中挖掘频繁出现的子图。大概…
Knowledge Discovery and Data MiningZhu, F., Yan, X., Han, J., Yu, P.S.: gPrune: A constraint pushing framework for graph pattern mining. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 388–400. Springer, Heidelberg (2007)...
答案里那篇论文的作者Prof. Edwin R Hancock老爷子是个Pattern Recognition领域的牛人(Vice President of IAPR, Editor-in-Chief of the journal Pattern Recognition, etc. )原本是物理学的 PhD，早年的研究领域是 High-energy Nuclear Physics, 后来去了约克大学的CS系开始研究Pattern Recognition和Computer Vision。
pattern discoverygraph mininggraph-structured patterninductive inferenceouterplanar graphRecently, due to the rapid growth of electronic data having graph structures such as HTML and XML texts and chemical compounds, many researchers have been interested in data mining and machine learning techniques for ...
Keywords: data mining,graph mining,graph database,frequent subgraph,jump pattern数据挖掘,图挖掘,图数据库,频繁子图,跳跃模式 Full-Text Cite this paper Add to My Lib Abstract: Many algorithms on subgraph mining have been proposed. However, the number of frequent subgraphs generated by these ...
Frequent substructure pattern mining has been an emerging data mining problem with many scientific and commercial applications. As a general data structure, labeled graph can be used to model much complicated substructure patterns among data. Given a graph dataset, D={G0, G1, ..., Gn}, support...
Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data
pattern recognition 模式识别 data mining 数据挖掘 object detection 目标检测 speech recognition 语音识别 molecule 分子 classification 分类 clustering 聚类 Spatial-Temporal Graph 时空图 eigenvalue decomposition 特征值分解 笔记 INTRODUCTION 引言 We proposea new taxonomy to divide the state-of-the-art graph ...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing ...
Finding dense subgraphs in a graph has a wide range of applications including correlation mining, fraud detection, e-commerce, bioinformatics, frequent pattern mining, and community detection, etc. In this talk, I will first introduce some representative applications, density measures and problems of ...