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2015 | OriginalPaper | Chapter

6. Mining Patterns in Different Related Databases

Authors : Animesh Adhikari, Jhimli Adhikari

Published in: Advances in Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

Effective data analysis using multiple databases requires highly accurate patterns. 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.

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previous chapter Mining Association Rules Induced by Item and Quantity Purchased
next chapter Mining Icebergs in Different Time-Stamped Data Sources
Literature
Adhikari A, Rao PR (2007a) Study of select items in multiple databases by grouping. In: Proceedings of the 3rd Indian international conference on artificial intelligence, pp 1699–1718
Adhikari A, Rao PR (2008a) Synthesizing heavy association rules from different real data sources. Pattern Recogn Lett 29(1):59–71CrossRef
Adhikari A, Rao PR (2008b) Efficient clustering of databases induced by local patterns. Decis Support Syst 44(4):925–943CrossRef
Adhikari A, Rao PR, Adhikari J (2007b) Mining multiple large databases. In: Proceedings of the 10th international conference on information technology, pp 80–84
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):774–778CrossRef
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):71–88CrossRefMATH
Zhang S, Wu X, Zhang C (2003) Multi-database mining. IEEE Comput Intell Bull 2(1):5–13
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, Zhang C, Yu JX (2004b) An efficient strategy for mining exceptions in multi-databases. Inf Sci 165(1–2):1–20MATH
Metadata
Title
Mining Patterns in Different Related Databases
Authors
Animesh Adhikari
Jhimli Adhikari
Copyright Year
2015
Book
Advances in Knowledge Discovery in Databases

Print ISBN: 978-3-319-13211-2

Electronic ISBN: 978-3-319-13212-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-13212-9_6

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