Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Data Mining

  • Jiawei HanEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_104


Data analysis; Knowledge discovery from data; Pattern discovery


Data miningis the process of discovering knowledge or patterns from massive amounts of data. As a young research field, data mining represents the confluence of a number of research fields, including database systems, machine learning, statistics, pattern recognition, high-performance computing, and specific application fields, such as WWW, multimedia, and bioinformatics, with broad applications. As an interdisciplinary field, data mining has several major research themes based on its mining tasks, including pattern-mining and analysis, classification and predictive modeling, cluster and outlier analysis, and multidimensional (OLAP) analysis. Data mining can also be categorized based on the kinds of data to be analyzed, such as multi-relational data mining, text mining, stream mining, web mining, multimedia (or image, video) mining, spatiotemporal data mining, information network analysis, biological...

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

Recommended Reading

  1. 1.
    Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2001.zbMATHGoogle Scholar
  2. 2.
    Han J, Kamber M. Data mining: concepts and techniques. 2nd ed. Amsterdam: Morgan Kaufmann; 2006.zbMATHGoogle Scholar
  3. 3.
    Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2001.CrossRefzbMATHGoogle Scholar
  4. 4.
    Tan P, Steinbach M, Kumar V. Introduction to data mining. Boston: Addison Wesley; 2005.Google Scholar
  5. 5.
    Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. Amsterdam: Morgan Kaufmann; 2005.zbMATHGoogle Scholar
  6. 6.
    Dasu T, Johnson T. Exploratory data mining and data cleaning. New York: Wiley; 2003.CrossRefzbMATHGoogle Scholar
  7. 7.
    Chakrabarti S. Mining the web: statistical analysis of hypertex and semi-structured data. Morgan Kaufmann; 2002.Google Scholar
  8. 8.
    Liu B. Web data mining: exploring hyperlinks, contents, and usage data. New York: Springer; 2006.zbMATHGoogle Scholar
  9. 9.
    Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 487–99.Google Scholar
  10. 10.
    Han J, Cheng H, Xin D, Yan X. Frequent pattern mining: current status and future directions. Data Min Knowl Disc. 2007;15(1):55–86.CrossRefMathSciNetGoogle Scholar
  11. 11.
    Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.zbMATHGoogle Scholar
  12. 12.
    Cheng H, Yan X, Han J, Yu PS. Direct discriminative pattern mining for effective classification. In: Proceedings of the 24th International Conference on Data Engineering; 2008. p. 169–78.Google Scholar
  13. 13.
    Zhang T, Ramakrishnan R, Livny M. BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data; 1996.p. 103–14.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA

Section editors and affiliations

  • Jian Pei
    • 1
  1. 1.School of Computing ScienceSimon Fraser Univ.BurnabyCanada