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Concluding Remarks

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Advances in Knowledge Discovery in Databases

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 79))

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Abstract

With the advancement of technologies, mass storage devices are now capable of storing more data. Also, they have become cheaper. Moreover varieties of data collection channels are now available in the market. Data mining is an emerging field of study, and has been applied to various domains.

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Notes

  1. 1.

    IEEE MSST: http://storageconference.org.

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 (2007a) A framework for synthesizing arbitrary boolean expressions Induced by frequent itemsets. In: Proceedings of 3rd Indian international conference on artificial intelligence, pp 5–23

    Google Scholar 

  • Adhikari A, Rao PR (2007b) Enhancing quality of knowledge synthesized from multi-database mining. Pattern Recogn Lett 28(16):2312–2324

    Article  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) Capturing association among items in a database. Data Knowl Eng 67(3):430–443

    Article  Google Scholar 

  • Adhikari A, Rao PR (2008c) Mining conditional patterns in a database. Pattern Recogn Lett 29(10):1515–1523

    Article  Google Scholar 

  • Adhikari A, Rao PR (2008d) Association rules induced by item and quantity purchased. In: Haritsa JR, Kotagiri R, and Pudi V (eds) Proceedings of international conference on database systems for advance applications. LNCS, vol 4947. pp 478–485

    Google Scholar 

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

    Article  Google Scholar 

  • Adhikari J, Rao PR (2010) Measuring influence of an item in a database over time. Pattern Recogn Lett 31(3):179–187

    Article  Google Scholar 

  • Adhikari A, Rao PR, Adhikari J (2007) Mining multiple large databases. In: Proceedings of the 10th international conference on information technology, pp 80–84

    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, Pedrycz W (2010) Developing multi-database mining applications. Springer, London

    Book  MATH  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 

  • Adhikari A, Adhikari J, Pedrycz W (2014) Data analysis and pattern recognition in multiple data sources. Springer, Switzerland

    Book  Google Scholar 

  • Aggarwal CC (ed) (2009) Managing and mining uncertain data. Springer, Heidelberg

    Google Scholar 

  • Aggarwal CC (ed) (2013) Managing and mining sensor data. Springer, Heidelberg

    Google Scholar 

  • Aggarwal CC, Yu PS (2008) Privacy-preserving data mining: models and algorithms. Springer, New York

    Google Scholar 

  • Aggarwal CC, Zhai CX (2012) Mining text data. Springer, Heidelberg

    Google Scholar 

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large databases (VLDB), pp 487–499

    Google Scholar 

  • Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD conference management of data, pp 207–216

    Google Scholar 

  • Antonie M-L, Zaïane OR (2004) Mining positive and negative association rules: an approach for confined rules. In: Proceedings of knowledge discovery in databases (PKDD), pp 27–38

    Google Scholar 

  • Banafa A (2014) The future of big data and analytics. http://www.linkedin.com

  • Bettini C, Jajodia S, Wang SX (2000) Time granularities in databases, data mining and temporal reasoning. Springer, Heidelberg

    Google Scholar 

  • Bourennani F (2011) Heterogeneous data mining. VDM, Saarbrücken

    Google Scholar 

  • Buyya R, Cortes T, Jin H (2001) High performance mass storage and parallel I/O: technologies and applications. Wiley, New Jersey

    Google Scholar 

  • Chu WW (2013) Data mining and knowledge discovery for big data: methodologies, challenge and opportunities. Springer, Heidelberg

    Google Scholar 

  • Chu WW (ed) (2014) Data mining and knowledge discovery for big data. Springer, Heidelberg

    Google Scholar 

  • Chu F, Zaniolo C (2004c) Fast and light boosting for adaptive mining of data streams. In: Proceedings on advances in knowledge discovery and data mining (PAKDD). Springer, Heidelberg, pp 282–292

    Google Scholar 

  • Chu F, Wang Y, Zaniolo C (2004a) An adaptive learning approach for noisy data streams. In: IEEE international conference on data mining (ICDM), pp 351–354

    Google Scholar 

  • Chu F, Wang Y, Zaniolo C (2004b) Mining noisy data streams via a discriminative model. In: Discovery science. Springer, Heidelberg, pp 47–59

    Google Scholar 

  • Gaber MM, Stahl F, Gomes JB (2014) Pocket data mining: big data on small devices. Springer, Heidelberg

    Google Scholar 

  • Han J, Pei J, Yiwen Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD conference management of data, pp 1–12

    Google Scholar 

  • He H, Ma Y (eds) (2013) Imbalanced learning: foundations, algorithms, and applications. Wiley, New Jersey

    Google Scholar 

  • Hearst MA (1999) Untangling text data mining. In: Proceedings of the 37th annual meeting of the association for computational linguistics (ACL)

    Google Scholar 

  • Hu H, Wang H, Zheng B (2011) Challenges in managing and mining large, heterogeneous data. In: Database systems for advanced applications (DASFAA) (2). Springer, Heidelberg, p 462

    Google Scholar 

  • Jusoh S, Alfawareh HM (2012) Techniques, applications and challenging issue in text mining. Int J Comput Sci Issues 9(6):1–6 ISSN: 1694-0814

    Google Scholar 

  • Kleinberg JM (2007) Challenges in mining social network data: processes, privacy, and paradoxes. In: Proceedings of international conference on Knowledge discovery and data mining (KDD), pp 4–5

    Google Scholar 

  • Li X, Ng A-K, Wang JTL (eds) (2014) Biological data mining and its applications in healthcare. World Scientific Publishing, Singapore

    Google Scholar 

  • Mahanta AK, Mazarbhuiya FA, Baruah HK (2005) Finding locally and periodically frequent sets and periodic association rules. In: Pattern recognition and machine intelligence LNCS, vol 3776. Springer, Heidelberg, pp 576–582

    Google Scholar 

  • Memon N, Xu JJ, Hicks DL, Chen H. (eds) (2010) Data Mining for social network data. Springer, New York

    Google Scholar 

  • Qin Z, Tang Y (2014) Uncertainty modeling for data mining

    Google Scholar 

  • Ranade S (1991) Mass storage technologies. Information Today

    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 

  • Stahl FT, Gaber MM, Bramer M, Yu PS (2010) Pocket Data Mining: Towards collaborative data mining in mobile computing environments. In: IEEE international conference on tools with artificial intelligence (ICTAI), vol 2 pp 323–330

    Google Scholar 

  • Takashi W, Jun L (eds) (2012) Emerging trends in knowledge discovery and data mining. LNCS, vol 7769. Springer, Heidelberg

    Google Scholar 

  • Wang W, Yang J (2010) Mining high-dimensional data. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook, Springer, New York

    Google Scholar 

  • Xiong H, Pandey G, Steinbach M, Kumar V (2006) Enhancing data analysis with noise removal. IEEE Trans Knowl Data Eng 18(2):304–319

    Article  Google Scholar 

  • Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604

    Article  Google Scholar 

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Correspondence to Animesh Adhikari .

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Adhikari, A., Adhikari, J. (2015). Concluding Remarks. 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_17

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  • DOI: https://doi.org/10.1007/978-3-319-13212-9_17

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