Abstract
It is widely recognized that successful businesses usually fall into set routines and become limited by their past. To remain successful, they need to discover new opportunities and niches. Niches are surprising rules that contradict the set routines; they capture significant, representative client sectors that deserve new, more profitable treatments; they are not merely strong-rule and exception pairs. In this paper we study the efficient mining of set routines and niches. We also introduce a semantic approach to select a set of representative patterns, and present an efficient incremental algorithm to implement the approach.
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
R.J. Bayardo. Efficiently mining long patterns from databases. In Proceedings of ACM-SIGMOD Int’l Conference on Management of Data, pages 85–93, 1998.
G. Dong and J. Li. Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In PAKDD, 1998.
G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. of ACM SIGKDD 1999.
G. Dong, X. Zhang, L. Wong, and J. Li. CAEP: Classification by aggregating emerging patterns. In Proc. Int’l Conference on Discovery Science, 1999.
KDNuggets. Data mining digs in, July 1999, American demographics. Also KDNuggets 99:17, http://www.kdnuggets.com/news/99/n17/i7.html, edited by Gregory Piatetsky-Shapiro.
J. Li, G. Dong, and K. Ramamohanarao. Instance-based classification by emerging patterns. In PKDD 2000.
J. Li, G. Dong, and K. Ramamohanarao. Making use of the most expressive jumping emerging patterns for classification. In PAKDD 2000.
B. Liu, L.-P. Ku, & W. Hsu. Discovering interesting holes in data. In IJCAI 1997.
Bing Liu, Wynne Hsu, Lai-Fun Mun, and Hing-Yan Lee. Finding interesting patterns using user expectations. TKDE, 11(6):817–832, 1999.
H. Liu, H. Lu, L. Feng, and F. Hussain. Efficient search of reliable exceptions. In PAKDD, 1999.
G. Piatesky-Shapiro and C.J. Matheus. The interestingness of deviations. In Proc. AAAI KDD Workshop, 1994.
S. Ramaswamy, S. Mahajan, and A. Silberschatz. On the discovery of interesting patterns in association rules. In VLDB, 1998.
Ron Rymon. Search through systematic set enumeration. In Proc. the Third Int’l Conference on Principles of Knowledge Representation and Reasoning, 1992.
E. Suzuki and Y. Kodratoff. Discovery of surprising exception rules based on intensity of implication. In PKDD, pages 10–18, 1998.
H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hätönen, and H. Mannila. Pruning and grouping discovered association rules. In MLnet Workshop on Statistics, Machine Learning, and Discovery in Databases, 1995.
X. Zhang, G. Dong, and K. Ramamohanarao. Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In KDD, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, G., Deshpande, K. (2001). Efficient Mining of Niches and Set Routines. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_27
Download citation
DOI: https://doi.org/10.1007/3-540-45357-1_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41910-5
Online ISBN: 978-3-540-45357-4
eBook Packages: Springer Book Archive