Skip to main content
SpringerLink
Log in

Data Mining in Databases: Languages and Indices

  • Chapter
  • First Online:
A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years

Part of the book series: Studies in Big Data ((SBD,volume 31))

  • 2000 Accesses

Abstract

Database systems methodologies and technology can provide a significant support to data mining processes. In this chapter we explore approaches which address the integration between data mining activities and DBMSs from different perspectives. More specifically, we focus on (i) specialized query languages which allow to define complex data mining tasks through the submission of query requests, and (ii) indices, i.e., physical data structures designed to improve the performance of mining algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M. Adnan, R. Alhajj, Drfp-tree: disk-resident frequent pattern tree. Appl. Intell. Springer 30(2), 84–97 (2009)

    Article  Google Scholar 

  2. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in VLDB ’94 (1994), pp. 487–499

    Google Scholar 

  3. R. Agrawal, R. Srikant, Mining sequential patterns, in International Conference on Data Engineering, Taipei, Taiwan, March 1995

    Google Scholar 

  4. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in Proc.ACM SIGMOD Conference on Management of Data(British Columbia, Washington, D.C., 1993), pp. 207–216

    Google Scholar 

  5. R. Agrawal, T. Imilienski, A. Swami, Mining association rules between sets of items in large databases, in SIGMOD’93, Washington DC, May 1993

    Google Scholar 

  6. E. Baralis, T. Cerquitelli, S. Chiusano, Index support for frequent itemset mining in a relational dbms, in ICDE (2005), pp. 754–765

    Google Scholar 

  7. E. Baralis, T. Cerquitelli, S. Chiusano, Imine: index support for item set mining. IEEE Trans. Knowl. Data Eng. 21(4), 493–506 (2009)

    Article  Google Scholar 

  8. E. Baralis, T. Cerquitelli, S. Chiusano, A. Grand, Scalable out-of-core itemset mining. Inf. Sci. 293, 146–162 (2015)

    Article  Google Scholar 

  9. G. Buehrer, S. Parthasarathy, A. Ghoting, Out-of-core frequent pattern mining on a commodity pc, in KDD ’06 (2006), pp. 86–95

    Google Scholar 

  10. Y.-L. Cheung, Mining frequent itemsets without support threshold: with and without item constraints. IEEE Trans. Knowl. Data Eng. 16(9), 1052–1069 (2004). Member-Ada Wai-Chee Fu

    Article  Google Scholar 

  11. G. Cong, B. Liu, Speed-up iterative frequent itemset mining with constraint changes, in ICDM (2002), pp. 107–114

    Google Scholar 

  12. M. El-Hajj, O.R. Zaiane, Inverted matrix: Efficient discovery of frequent items in large datasets in the context of interactive mining. in ACM SIGKDD (2003)

    Google Scholar 

  13. A. Ghoting, G. Buehrer, S. Parthasarathy, D. Kim, A. Nguyen, Y.-K. Chen, P. Dubey, Cache-conscious frequent pattern mining on modern and emerging processors. VLDB J. 16(1), 77–96 (2007)

    Article  Google Scholar 

  14. G. Grahne, J. Zhu, Efficiently using prefix-trees in mining frequent itemsets, in FIMI, November 2003

    Google Scholar 

  15. G. Grahne, J. Zhu, Mining frequent itemsets from secondary memory, in ICDM ’04 (IEEE Computer Society, Washington, DC, USA, 2004), pp. 91–98

    Google Scholar 

  16. J. Han, Y. Fu, W. Wang, K. Koperski, O. Zaiane, DMQL: a data mining query language for relational databases, in Proceedings of SIGMOD-96 Workshop on Research Issues on Data Mining and Knowledge Discovery (1996)

    Google Scholar 

  17. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in SIGMOD ’00 (2000), pp. 1–12

    Google Scholar 

  18. T. Imielinski, H. Mannila, A database perspective on knowledge discovery. Commun. ACM 39(11), 58–64 (1996)

    Article  Google Scholar 

  19. T. Imieliński, A. Virmani, Msql: a query language for database mining. Data Min. Knowl. Disc. 3(4), 373–408 (1999)

    Article  Google Scholar 

  20. B. Lan, B.C. Ooi, K.-L. Tan, Efficient indexing structures for mining frequently patterns, in IEEE ICDE (2002)

    Google Scholar 

  21. C.K.-S. Leung, L.V.S. Lakshmanan, R.T. Ng, Exploiting succinct constraints using fp-trees. SIGKDD Explor. Newsl. 4(1), 40–49 (2002)

    Article  Google Scholar 

  22. B. Liu, W. Hsu, Y. Ma, Integrating classification and association rule mining, in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, KDD’98 (AAAI Press, 1998), pp. 80–86

    Google Scholar 

  23. C. Lucchese, S. Orlando, R. Perego, kdci: on using direct count up to the third iteration, in FIMI (2004)

    Google Scholar 

  24. H. Mannila, H. Toivonen, A. Inkeri Verkamo, Efficient algorithms for discovering association rules, in KDD Workshop (1994), pp. 181–192

    Google Scholar 

  25. R. Meo, Optimization of a language for data mining, in Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, Florida, 2003

    Google Scholar 

  26. R. Meo, G. Psaila, S. Ceri, A new SQL-like operator for mining association rules, in Proceedings of the 22st VLDB Conference, Bombay, India, September 1996

    Google Scholar 

  27. R. Meo, M. Botta, R. Esposito, Query Rewriting in Itemset Mining (Springer, Berlin, 2004), pp. 111–124

    Google Scholar 

  28. S. Orlando, C. Lucchese, P. Palmerini, R. Perego, F. Silvestri, kDCI: a multi-strategy algorithm for mining frequent sets, in FIMI (2003)

    Google Scholar 

  29. J. Pei, J. Han, L.V.S. Lakshmanan, Pushing convertible constraints in frequent itemset mining. Data Min. Knowl. Discov. 8(3), 227–252 (2004)

    Article  MathSciNet  Google Scholar 

  30. PostgreSQL. Postgresql, http://www.postgresql.org

  31. G. Ramesh, W.A. Maniatty, M.J. Zaki, Indexing and data access methods for database mining, in DMKD (2002)

    Google Scholar 

  32. A. Savasere, E. Omiecinski, S.B. Navathe, An efficient algorithm for mining association rules in large databases, in VLDB (1995), pp. 432–444

    Google Scholar 

  33. R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in KDD (1997), pp. 67–73

    Google Scholar 

  34. H. Toivonen, Sampling large databases for association rules, in VLDB (1996), pp. 134–145

    Google Scholar 

  35. T. Uno, M. Kiyomi, H. Arimura, LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets, in FIMI ’04 (2004)

    Google Scholar 

  36. M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)

    Article  Google Scholar 

  37. L. Zhao, M.J. Zaki, N. Ramakrishnan, BLOSOM: a framework for mining arbitrary boolean expressions, in KDD ’06 (ACM Press, New York, NY, USA, 2006), pp. 827–832

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Politecnico di Torino, corso Duca degli Abruzzi 24, Torino, Italy

    Elena Baralis, Tania Cerquitelli & Silvia Chiusano

  2. Università di Torino, corso Svizzera 185, Torino, Italy

    Rosa Meo

Authors
  1. Elena Baralis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tania Cerquitelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvia Chiusano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rosa Meo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosa Meo .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Sergio Flesca

  2. University of Calabria, Rende, Italy

    Sergio Greco

  3. ICAR-CNR, Rende, Italy

    Elio Masciari

  4. University of Calabria, Rende, Italy

    Domenico Saccà

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Baralis, E., Cerquitelli, T., Chiusano, S., Meo, R. (2018). Data Mining in Databases: Languages and Indices. In: Flesca, S., Greco, S., Masciari, E., Saccà, D. (eds) A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years. Studies in Big Data, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-319-61893-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61893-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61892-0

  • Online ISBN: 978-3-319-61893-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation