The Community for Technology Leaders
2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 357-360
Chih-Hua Tai , Dept. of CSIE, National Taipei University, New Taipei, Taiwan
Tsung-Han Lee , Institute of Information Science, Academia Sinica, Taipei, Taiwan
Sheng-Hao Chiang , Institute of Information Science, Academia Sinica, Taipei, Taiwan
Jui-Yi Tsai , Institute of Information Science, Academia Sinica, Taipei, Taiwan
De-Nian Yang , Institute of Information Science, Academia Sinica, Taipei, Taiwan
Yi-Hsin Wu , Institute for Information Industry, Taipei, Taiwan
Ya-Huei Chan , Institute for Information Industry, Taipei, Taiwan
ABSTRACT
Inspired by the coming of data-driven innovation and economy, an increasing number of companies over the world are eager to analyze their data for creating useful knowledge, while graph data have become more and more crucial in many areas, such as social networks and medical/chemical applications. Different from conventional transaction data, finding the frequent patterns in a graph is more challenging because graph structures are much more flexible and generalized, and various algorithms have been proposed to properly cope with different graph data. However, for companies and organizations without sophisticated and experienced data scientists, it is usually difficult for them to properly choose a graph mining algorithm that is the most efficient and effective one for their own data. When an inadequate algorithm is employed, excess processing time is usually incurred, and important large patterns may not always be able to be generated. To address the above important need, this paper proposes a new mechanism, referred to as GMRecommend, for recommending a proper graph mining algorithm given the graph data with specific features. GMRecommend is based on support vector machine (SVM) by incorporating two important categories of features: graph features and pattern features. Experimental results manifest that GMRecommend can effectively choose the most proper graph mining algorithm for different kinds of graph data with different characteristics and requirements.
INDEX TERMS
Data mining, Algorithm design and analysis, Classification algorithms, Feature extraction, Companies, Support vector machines
CITATION

Chih-Hua Tai et al., "On recommendation of graph mining algorithms for different data," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 357-360.
doi:10.1109/BIGCOMP.2016.7425947
96 ms
(Ver 3.3 (11022016))