.png){kind=icon}
返回
An Iterative MapReduce Based Frequent Subgraph Mining Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2014.2345408.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Frequent subgraph mining (FSM) is an important task for exploratory data analysis on graph data. Over the years, many algorithms have been proposed to solve this task. These algorithms assume that the data structure of the mining task is small enough to fit in the main memory of a computer. However, as the real-world graph data grows, both in size and quantity, such an assumption does not hold any longer. To overcome this, some graph database-centric methods have been proposed in recent years for solving FSM; however, a distributed solution using MapReduce paradigm has not been explored extensively. Since MapReduce is becoming the de-facto paradigm for computation on massive data, an efficient FSM algorithm on this paradigm is of huge demand. In this work, we propose a frequent subgraph mining algorithm called FSM-H which uses an iterative MapReduce based framework. FSM-H is complete as it returns all the frequent subgraphs for a given user-defined support, and it is efficient as it applies all the optimizations that the latest FSM algorithms adopt. Our experiments with real life and large synthetic datasets validate the effectiveness of FSM-H for mining frequent subgraphs from large graph datasets. The source code of FSM-H is available from www.cs.iupui.edu/similar to alhasan/software/
Keyword:
Frequent sub-graph mining
iterative MapReduce
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
CD164 regulates proliferation, progression, and invasion of human glioblastoma cells.png)
Oncotarget
OAAI
OncotargetIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容