Abstract
Mining the frequent pattern from data set is one of the key success stories of data mining research. Currently, most of the efforts are focused on the independent data such as the items in the marketing basket. However, the objects in the real world often have close relationship with each other. How to gain the frequent pattern from these relations is the objective in this paper. We use graphs to model the relations, and select a simple type for analysis. Combining the graph-theory and algorithms to generate frequent patterns, a new structure SFP-Tree and an algorithm, which can mine these simple graphs efficiently, have been proposed. We evaluate the performance of the algorithm by experiments with synthetic datasets. The empirical results show that the SFP can do the job well. At the end of this paper, the potential improvement of SFP is mentioned.
This paper was supported by the Key Program of National Natural Science Foundation of China(No.69933010)
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ramesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad. Depth first generation of long patterns. KDD 2000: 108–118
Ramesh C. Agarwal, Charu C. Aggarwal, V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. In J. of Parallel and Distribute Computing, 2000.
Rakesh Agrawal, Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487–499
Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Data. SIGMOD Conference 1997: 255–264
Jiawei Han, Jian Pei, Yiwen Yin. Mining Frequent Patterns without Candidate Generation. SIGMOD Conference 2000: 1–12
Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. PDKK 2000: 13–23
Michihiro Kuramochi, George Karypis. Frequent Subgraph Discovery. ICDM 2001: 313–320
Heikki Mannila, Hannu Toivonen. Levelwise Search and Borders of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery 1(3): 241–258 (1997)
Jong Soo Park, Ming-Syan Chen, Philip S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175–186
Mike Perkowitz, Oren Etzioni. Adaptive Sites: Automatically learning from user access patterns. In Proc. 6th Int. World Wide Web Conf.(WWW’97), Santa Clara, California, April 1997.
J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-Mine: Hyper-Structure Proc. 2001 Int. Conf. on Data Mining (ICDM’01), San Jose, CA, Nov. 2001
J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proc. 2001 Int. Conf. on Data Engineering (ICDE’01), Heidelberg, Germany, April 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuan, Q., Lou, Y., Zhou, H., Wang, W., Shi, B. (2002). Extract Frequent Pattern from Simple Graph Data. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_15
Download citation
DOI: https://doi.org/10.1007/3-540-45703-8_15
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44045-1
Online ISBN: 978-3-540-45703-9
eBook Packages: Springer Book Archive