Abstract
The model of local pattern analysis provides sound solutions to many multi-database mining problems. In this chapter, we discuss different types of extreme association rules in multiple databases viz., heavy association rule , high-frequency association rule , low-frequency association rule, and exceptional association rule .
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Adhikari A, Ramachandrarao P, Pedrycz W (2010) Developing multi-databases mining applications. Springer, Berlin
Adhikari A, Rao PR (2008) Synthesizing heavy association rules from different real data sources. Pattern Recogn Lett 29(1):59–71
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD conference, pp 207–216
Agrawal R, Shafer J (1999) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962–969
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of international conference on very large data bases, pp 487–499
Chattratichat J, Darlington J, Ghanem M, Guo Y, Hüning H, Köhler M, Sutiwaraphun J, To HW, Yang D (1997) Large scale data mining: challenges, and responses. In: Proceedings of the third international conference on knowledge discovery and data mining, pp 143–146
Cheung D, Ng V, Fu A, Fu Y (1996) Efficient mining of association rules in distributed databases. IEEE Trans Knowl Data Eng 8(6):911–922
Frequent itemset mining dataset repository (2004) http://fimi.cs.helsinki.fi/data
Han J, Pei J, Yiwen Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD conference on management of data, pp 1–12
Last M, Kandel A (2001) Automated detection of outliers in real-world data. In: Proceedings of the second international conference on intelligent technologies, pp 292–301
Liu H, Lin F, He J, Cai Y (2010) New approach for the sequential pattern mining of high-dimensional sequence databases. Decis Support Syst 50(1):270–280
Pyle D (1999) Data preparation for data mining. Morgan Kufmann, San Francisco
Ramkumar T, Srivinasan R (2008) Modified algorithms for synthesizing high-frequency rules from different data sources. Knowl Inf Syst 17(3):313–334
Rozenberg B, Gudes E (2006) Association rules mining in vertically partitioned databases. Data Knowl Eng 59(2):378–396
Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st international conference on very large data bases, pp 432–443
Shang S, Dong X, Li J, Zhao Y (2008) Mining positive and negative association rules in multi-database based on minimum interestingness. In: Proceedings of the 2008 international conference on intelligent computation technology and automation, pp 791–794
Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 14(2):353–367
Wu X, Zhu X, He Y, Abdullah N, Arslan AN (2013) PMBC: pattern mining from biological sequences with wildcard constraints. Comp Bio Med 43(5):481–492
Yi X, Zhang Y (2007) Privacy-preserving distributed association rule mining via semi-trusted mixer. Data Knowl Eng 63(2):550–567
Zeng L, Li L, Duan L, Lü K, Shi Z, Wang M, Wu W, Luo P (2012) Distributed data mining: a survey. Inf Technol Manage 13(4):403–409
Zhang S, Wu X, Zhang C (2003) Multi-database mining. IEEE Comput Intell Bull 2(1):5–13
Zhang S, You X, Jin Z, Wu X (2009) Mining globally interesting patterns from multiple databases using kernel estimation. Expert Syst Appl Int J 36(8):10863–10869
Zhang S, Wu X (2011) Fundamentals of association rules in data mining and knowledge discovery. Wiley Interdisc Rev Data Min Knowl Discov 1(2):97–116
Zhong N, Yao YYY, Ohishima M (2003) Peculiarity oriented multidatabase mining. IEEE Trans Knowl Data Eng 15(4):952–960
Zhu X, Wu X (2007) Discovering relational patterns across multiple databases. In: Proceedings of ICDE, pp 726–735
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Adhikari, A., Adhikari, J. (2015). Synthesizing Some Extreme Association Rules from Multiple Databases. In: Advances in Knowledge Discovery in Databases. Intelligent Systems Reference Library, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-319-13212-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-13212-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13211-2
Online ISBN: 978-3-319-13212-9
eBook Packages: EngineeringEngineering (R0)