Abstract
Effective data analysis using multiple databases requires patterns that are almost error-free. As the local pattern analysis might extract patterns of low quality from multiple databases, it becomes necessary to improve mining multiple databases. In this chapter, we present an idea of multi-database mining by making use of local pattern analysis. We elaborate on the existing specialized and generalized techniques which are used for mining multiple large databases.
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 (2013) Clustering local frequency items in multiple databases. Inf Sci 237:221–241
Adhikari A (2012) Synthesizing global exceptional patterns in different data sources. J Intelli Syst 21(3):293–323
Adhikari A, Rao PR (2008a) Synthesizing heavy association rules from different real data sources. Pattern Recogn Lett 29(1):59–71
Adhikari A, Rao PR (2008b) Efficient clustering of databases induced by local patterns. Decis Support Syst 44(4):925–943
Adhikari A, Ramachandrarao P, Pedrycz W (2011) Study of select items in different data sources by grouping. Knowl Inf Syst 27(1):23–43
Adhikari A, Ramachandrarao P, Prasad B, Adhikari J (2010) Mining multiple large data sources. Int Arab J Inf Technol 7(2):241–249
Adhikari J, Rao PR, Adhikari A (2009) Clustering items in different data sources induced by stability. Int Arab J Inf Technol 6(4):66–74
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of international conference on very large data bases, pp 487–499
Babcock B, Chaudhury S, Das G (2003) Dynamic sample selection for approximate query processing. In: Proceedings of ACM SIGMOD conference management of data, pp 539–550
Coenen F, Leng P, Ahmed S (2004) Data structure for association rule mining: T-trees and P-trees. IEEE Trans Knowl Data Eng 16(6):774–778
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
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
Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 14(2):353–367
Wu X, Zhang C, Zhang S (2005) Database classification for multi-database mining. Inf Syst 30(1):71–88
Zhang C, Liu M, Nie W, Zhang S (2004a) Identifying global exceptional patterns in multi-database mining. IEEE Comput Intell Bull 3(1):19–24
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 36(8):10863–10869
Zhang S, Zhang C, Yu JX (2004b) An efficient strategy for mining exceptions in multi-databases. Inf Sci 165(1–2):1–20
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Adhikari, A., Adhikari, J., Pedrycz, W. (2014). Synthesizing Global Patterns in Multiple Large Data Sources. In: Data Analysis and Pattern Recognition in Multiple Databases. Intelligent Systems Reference Library, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-03410-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-03410-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03409-6
Online ISBN: 978-3-319-03410-2
eBook Packages: EngineeringEngineering (R0)