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Introduction

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Data Analysis and Pattern Recognition in Multiple Databases

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 61))

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Abstract

Organizations that collect data from their multiple branches are common. Also, many established organizations possess data for a long period of time. Due to a spectrum of analyses, such data often need to be sub-divided into smaller databases.

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References

  • Adhikari A (2012) Synthesizing global exceptional patterns in different data sources. J Intell Syst 21(3):293–323

    Google Scholar 

  • Adhikari A, Rao PR (2008a) Synthesizing heavy association rules from different real data sources. Pattern Recogn Lett 29(1):59–71

    Article  Google Scholar 

  • Adhikari A, Rao PR (2008b) Efficient clustering of databases induced by local patterns. Decis Support Syst 44(4):925–943

    Article  Google Scholar 

  • Adhikari J, Rao PR (2013) Identifying calendar-based periodic patterns. In: Ramanna S, Jain L, Howlett RJ (eds) Emerging paradigms in machine learning, pp 329–357. Springer, Berlin

    Google Scholar 

  • Adhikari J, Rao PR, Adhikari A (2009) Clustering items in different data sources induced by stability. Int Arab J Inf Technol 6(4):394–402

    Google Scholar 

  • Adhikari A, Ramachandrarao P, Prasad B, Adhikari J (2010a) Mining multiple large data sources. Int Arab J Inf Technol 7(2):241–249

    Google Scholar 

  • Adhikari A, Ramachandrarao P, Pedrycz W (2010b) Developing multi-databases mining applications. Springer, London

    Google Scholar 

  • Adhikari A, Ramachandrarao P, Pedrycz W (2011a) Study of select items in different data sources by grouping. Knowl Inf Syst 27(1):23–43

    Article  Google Scholar 

  • Adhikari J, Rao PR, Pedrycz W (2011b) Mining icebergs in time-stamped databases. In: Proceedings of Indian international conferences on artificial intelligence, pp 639–658

    Google Scholar 

  • Agrawal R, Shafer J (1999) Parallel mining of association rules. IEEE Trans Knowl Data Eng 8(6):962–969

    Article  Google Scholar 

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of international conference on very large data bases, pp 487–499

    Google Scholar 

  • Ashok VG, Mukkamala R (2011) Data mining without data: a novel approach to privacy-preserving collaborative distributed data mining. Workshop on Privacy in the Electronic Society, pp 159–164

    Google Scholar 

  • 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

    Google Scholar 

  • Böttcher M, Hoppner F, Spiliopoulou M (2008) On exploiting the power of time in data mining. SIGKDD Explor 10(2):3–11

    Article  Google Scholar 

  • Chen Y-L, Tang K, Shen R-J, Hu Y-H (2005) Market basket analysis in a multiple store environment. Decis Support Syst 40(2):39–354

    Google Scholar 

  • Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New York

    MATH  Google Scholar 

  • Congiusta A, Talia D, Trunfio P (2007) Service-oriented middleware for distributed data mining on the grid. J Parallel Distrib Comput 68(1):3–15

    Article  Google Scholar 

  • Da Silva JC, Klusch M (2006) Inference in distributed data clustering. Eng Appl Artif Intell 19(4):363–369

    Article  Google Scholar 

  • Da Silva JC, Giannellab C, Bhargava R, Kargupta H, Klusch M (2005) Distributed data mining and agents. Eng Appl Artif Intell 18(7):791–807

    Article  Google Scholar 

  • Domingos P (2003) Prospects and challenges for multi-relational data mining. SIGKDD Explor 5(1):80–83

    Article  Google Scholar 

  • Dumas M, Fauvet MC, Scholl PC (1998) Handling temporal grouping and pattern-matching queries in a temporal object model. In: Proceedings of CIKM, pp 424–431

    Google Scholar 

  • Dzeroski S (2003) Multi-relational data mining: an introduction. SIGKDD Explor 5(1):1–16

    Article  Google Scholar 

  • Ezeife CI, Zhang D (2009) TidFP: mining frequent patterns in different databases with transaction ID. In: Proceedings of DaWaK, pp 125–137

    Google Scholar 

  • Fiolet V, Toursel B (2007) A clustering method to distribute a database on a grid. Future Gener Comput Syst 23(8):997–1002

    Article  Google Scholar 

  • Forestier G, Wemmert C, Pierre Gançarski P, Inglada J (2009) Mining multiple satellite sensor data using collaborative clustering. In: Proceedings of ICDM workshops, pp 501–506

    Google Scholar 

  • Foster I, Kesselman C (eds) (1999) The grid: blueprint for a future computing infrastructure. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Greenfield A (2006) Everyware: the dawning age of ubiquitous computing, 1st edn. New Riders Publishing, Indianapolis

    Google Scholar 

  • Han J, Nishio S, Kawano H, Wang W (1998) Generalization-based data mining in object-oriented databases using an object cube model. Data Knowl Eng 25(1–2):55–97

    Article  MATH  Google Scholar 

  • Hu J, Zhong N (2006) Organizing multiple data sources for developing intelligent e-business portals. Data Min Knowl Disc 12(2–3):127–150

    Article  MathSciNet  Google Scholar 

  • Inan A, Kaya SV, Saygın Y, Savas E, Hintoglu AA, Levi A (2007) Privacy preserving clustering on horizontally partitioned data. Data Knowl Eng 63(3):646–666

    Article  Google Scholar 

  • Jiang Z, Sarkar S, De P, Dey B (2007) A framework for reconciling attribute values from multiple data sources. Manage Sci 53(12):1946–1963

    Article  Google Scholar 

  • Kargupta H, Huang W, Krishnamurthy S, Park B, Wang S (2000) Collective PCA from distributed and heterogeneous data. In: Proceedings of the 4th European conference on principles and practice of knowledge discovery in databases, pp 452–457

    Google Scholar 

  • Kargupta H, Liu K, Ryan J (2003) Privacy sensitive distributed data mining from multi-party data. In: Proceedings of intelligence and security informatics, pp 336–342

    Google Scholar 

  • Kargupta H, Joshi A, Sivakumar K, Yesha Y (2004) Data mining: next generation challenges and future directions. MIT Press, Cambridge

    Google Scholar 

  • Kargupta H, Han J, Yu PS, Motwani R, Kumar V (2008) Next generation of data mining. Springer, Berlin

    Google Scholar 

  • Kum H-C, Chang HC, Wang W (2006) Sequential pattern mining in multi-databases via multiple alignment. Data Min Knowl Disc 12(2–3):151–180

    Article  MathSciNet  Google Scholar 

  • Lan G-C, Hong T-P, Tseng VS (2007) A novel algorithm for mining rare-utility itemsets in a multi-database environment. In: Proceedings of the 26th workshop on combinatorial mathematics and computation theory, pp 293–302

    Google Scholar 

  • Li H, Shen Y, Hu X (2009) A novel mining method of global negative association rules in multi-database. In: Proceedings of IEEE international conference on intelligent computing and intelligent systems, pp 392–396

    Google Scholar 

  • Ling CX, Yang Q (2006) Discovering classification from data of multiple sources. Data Min Knowl Disc 12(2–3):181–201

    Article  MathSciNet  Google Scholar 

  • Liu H, Lu H, Yao J (2001) Toward multi-database mining: identifying relevant databases. IEEE Trans Knowl Data Eng 13(4):541–553

    Article  Google Scholar 

  • Luo J, Wang M, Hu J, Shi J (2007) Distributed data mining on agent grid: issues, platform and development toolkit. Future Gener Comput Syst 23(1):61–68

    Article  Google Scholar 

  • Moon B, Lopez IFV, Immanuel V (2003) Efficient algorithms for large-scale temporal aggregation. IEEE Trans Knowl Data Eng 15(3):744–759

    Article  Google Scholar 

  • Ozsu MT, Valduriez P (2011) Principles of distributed database systems. Springer, Berlin

    Google Scholar 

  • Page D, Craven M (2003) Biological applications of multi-relational data mining. SIGKDD Explor 5(1):69–79

    Article  Google Scholar 

  • Papadimitriou S, Sun J, Faloutsos C (2005) Streaming pattern discovery in multiple time-series. In: Proceedings of VLDB, pp 697–708

    Google Scholar 

  • Peng W-C, Liao Z-X (2009) Mining sequential patterns across multiple sequence databases. Data Knowl Eng 68(10):1014–1033

    Article  Google Scholar 

  • Pyle D (1999) Data preparation for data mining. Morgan Kaufmann, San Francisco

    Google Scholar 

  • 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

    Google Scholar 

  • Siadaty MS, Harrison JH Jr (2008) Multi-database mining. Clin Lab Med 28(1):73–82

    Article  Google Scholar 

  • Stankovski V, Swain M, Kravtsov V, Niessen T, Wegener D, Kindermann J, Dubitzky W (2008) Grid-enabling data mining applications with DataMiningGrid: an architectural perspective. Future Generation Computer Systems 24(4):259–279

    Article  Google Scholar 

  • Stolfo S, Prodromidis AL, Chan PK (1997) JAM: java agents for meta-learning over distributed databases. In: Proceedings of 3rd international conference on knowledge discovery and data mining, pp 74–81

    Google Scholar 

  • Su K, Huang H, Wu X, Zhang S (2006) A logical framework for identifying quality knowledge from different data sources. Decis Support Syst 42(3):1673–1683

    Article  Google Scholar 

  • Tan P-N, Kumar V, Steinbach M (2006) Introduction to data mining. Pearson Education, Boston

    Google Scholar 

  • Wang JT, Zaki MJ, Toivonen HT, Shasha DE (2005) Data mining in bioinformatics. Springer, London

    Google Scholar 

  • Wilkinson (2009) Grid computing: techniques and applications. CRC Press, Boca Raton

    Google Scholar 

  • Wu X, Zhang S (2003) Synthesizing high-frequency rules from different data sources. IEEE Trans Knowl Data Eng 14(2):353–367

    Google Scholar 

  • Wu X, Zhang C, Zhang S (2005) Database classification for multi-database mining. Inf Syst 30(1):71–88

    Article  MATH  Google Scholar 

  • Yan J, Liu N, Yang Q, Zhang B, Cheng Q, Chen Z (2006) Mining adaptive ratio rules from distributed data sources. Data Min Knowl Disc 12(2–3):249–273

    Article  MathSciNet  Google Scholar 

  • Yi X, Zhang Y (2007) Privacy-preserving distributed association rule mining via semi-trusted mixer. Data Knowl Eng 63(2):550–567

    Article  Google Scholar 

  • Yin X, Han J (2005) Efficient classification from multiple heterogeneous databases. In: Proceedings of 9th European conference on principles and practice of knowledge discovery in databases, pp 404–416

    Google Scholar 

  • Zhan J, Matwina S, Chang LW (2006) Privacy-preserving collaborative association rule mining. J Netw Comput Appl 30(3):1216–1227

    Article  Google Scholar 

  • Zhang S, Wu X, Zhang C (2003) Multi-database mining. IEEE Comput Intell Bull 2(1):5–13

    Google Scholar 

  • 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

    Google Scholar 

  • Zhang S, Zhang C, Wu X (2004b) Knowledge discovery in multiple databases. Springer, London

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Zhao F, Guibas L (2004) Wireless sensor networks: an information processing approach. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Zhong S (2007) Privacy-preserving algorithms for distributed mining of frequent itemsets. Inf Sci 177(2):490–503

    Article  MATH  Google Scholar 

  • Zhong N, Yao YY, Ohshima M, Ohsuga S (2001) Interestingness, peculiarity, and multi-database mining. In: Proceedings of ICDM, pp 566–576

    Google Scholar 

  • Zhu X, Wu X (2007) Discovering relational patterns across multiple databases. In: Proceedings of ICDE, pp 726–735

    Google Scholar 

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Authors and Affiliations

  1. Parvatibai Chowgule College, P. O. Fatorda, Margao, Goa, 403 602, India

    Animesh Adhikari

  2. Narayan Zantye College, P. O. Bicholim Industrial Estate, Bicholim, 403 529, India

    Jhimli Adhikari

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, ABT6G2V4, Canada

    Witold Pedrycz

Authors
  1. Animesh Adhikari
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  2. Jhimli Adhikari
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  3. Witold Pedrycz
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Correspondence to Animesh Adhikari .

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Adhikari, A., Adhikari, J., Pedrycz, W. (2014). Introduction. 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_1

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  • DOI: https://doi.org/10.1007/978-3-319-03410-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03409-6

  • Online ISBN: 978-3-319-03410-2

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