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Mining concise patterns on graph-connected itemsets

文献类型：期刊论文

作者Niu, Qiang2; Zhang, Di4; Zhang, Yunquan3,4; Qiu, Xingbao1
刊名NEUROCOMPUTING
出版日期2019-04-07
卷号336页码:27-35
关键词Pattern mining MDL Graph Diffusion kernel Maximal entropy random walk
ISSN号0925-2312
DOI10.1016/j.neucom.2018.03.084
英文摘要The itemset is a basic and usual form of data. People can obtain new insights into their business by discovering its implicit regularities through pattern mining. In some real applications, e.g., network alarm association, the itemsets usually have the following two characteristics: (1) the observed samples come from different entities, with inherent structural relationships implied in their static properties; (2) the samples are scarce, which may lead to incomplete pattern extraction. This paper considers how to efficiently find a concise set of patterns on such kind of data. Firstly, we use a graph to express the entities and their interconnections and propagate every sample to every node with a weight, determined by the pre-defined combination of kernel functions based on the similarities of the nodes and patterns. Next, the weight values can be naturally imported into the MDL-based filtering process and bring a differentiated pattern set for each node. Experiments show that the solution can outperform the global solution (trading all nodes as one) and isolated solution (removing all edges) on simulated and real data, and its effectiveness and scalability can be further verified in the application of large-scale network operation and maintenance. (C) 2018 Elsevier B.V. All rights reserved.
资助项目NSF of China[11301420] ; NSF of Jiangsu Province[BK20150373] ; NSF of Jiangsu Province[BK20171237] ; Suzhou Science and Technology Program[SZS201613] ; XJTLU Key Programme Special Fund (KSF)
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000461358600004
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/4125]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Di
作者单位1.China Mobile Commun Corp, Beijing 100032, Peoples R China
2.Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou 215123, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
4.Commun Univ China, Sch Comp Sci, Beijing 100024, Peoples R China
推荐引用方式
GB/T 7714		 Niu, Qiang,Zhang, Di,Zhang, Yunquan,et al. Mining concise patterns on graph-connected itemsets[J]. NEUROCOMPUTING,2019,336:27-35.
APA Niu, Qiang,Zhang, Di,Zhang, Yunquan,&Qiu, Xingbao.(2019).Mining concise patterns on graph-connected itemsets.NEUROCOMPUTING,336,27-35.
MLA Niu, Qiang,et al."Mining concise patterns on graph-connected itemsets".NEUROCOMPUTING 336(2019):27-35.

入库方式： OAI收割

来源：计算技术研究所

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