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Data mining in a nutshell

Author:

Saěso DězeroskiAuthors Info & Claims

Relational Data Mining

October 2001

Pages 3 - 27

Published: 05 October 2001 Publication History

Abstract

Data mining, the central activity in the process of knowledge discovery in databases, is concerned with finding patterns in data. This chapter introduces and illustrates the most common types of patterns considered by data mining approaches and gives rough outlines of the data mining algorithms that are most frequently used to look for such patterns. It also briefly introduces relational data mining, starting with patterns that involve multiple relations and laying down the basic principles common to relational data mining algorithms. An overview of the contents of this book is given, as well as pointers to literature and Internet resources on data mining.

References

[1]

P. Adriaans and D. Zantinge. Data Mining. Addison-Wesley, Reading, 1996. 1.2 R. Agrawal, T. Tmielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SICMOD Conference on Management of Data, pages 207-216. ACM Press, New York, 1993.

Digital Library

[2]

M.J.A. Berry and C. Linoff. Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley and Sons, New York, 1997.

Digital Library

[3]

M.J.A. Berry and C. Linoif. Mastering Data Mining: The Art and Science of Customer Relationship Management. John Wiley and Sons, New York, 1999.

Digital Library

[4]

A. Berson and 3.3. Smith. Data Warehousing, Data Mining and OLAP. McGraw-Hill, New York, 1997.

Digital Library

[5]

M. Berthold and D.J. Hand, editors. Intelligent Data Analysis: An Introduction. Springer, Berlin, 1999.

Digital Library

[6]

L. Breiman, 3. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984.

[7]

B. Cestnik. Estimating probabilities: A crucial task in machine learning. In Proceedings of the Ninth European Conference on Artificial Intelligence, pages 147-149. Pitman, London.

[8]

P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Proceedings of the Fifth European Working Session on Learning, pages 151-163. Springer, Berlin, 1991.

Digital Library

[9]

B. V. Dasarathy, editor. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1990.

[10]

S. Dzeroski, L. Todorovski, I. Bratko, B. Kompare, and V. Krizman. Equation discovery with ecological applications. In A.H. Fielding, editor, Machine Learning Methods for Ecological Applications, pages 185-207. Kluwer, Boston, 1999.

[11]

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. Ftom data mining to knowledge discovery: An overview. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 1-34. MIT Press, Cambridge, MA, 1996.

Digital Library

[12]

U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and It. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge, MA, 1996.

Digital Library

[13]

W. Frawley, G. Piatetsky-Shapiro, and C. Matheus. Knowledge discovery in databases: An overview. In G. Piatetsky-Shapiro and W. Frawley, editors, Knowledge Discovery in Databases, pages 1-27. MIT Press, Cambridge, MA, 1991.

[14]

R. Groth. Data Mining: A Hands-On Approach for Business Professionals Prentice Hall, Upper Saddle River, NJ, 1997.

Digital Library

[15]

J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, CA, 2001.

Digital Library

[16]

R.V. Flogg and A.T. Craig. Introduction to Mathematical Statistics, 5th edition. Prentice Hall, Englewood Cliffs, NJ, 1995.

[17]

L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley & Sons, New York, 1990.

[18]

N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994. Freely available at http://wwwai.ijs.si/SasoDzeroski/ILPBook/.

Digital Library

[19]

R.S. Michalski, I. Bratko, and M. Kubat, editors, Machine Learning, Data Mining and Knowledge Discovery: Methods and Applications. John Wiley and Sons, Chichester, 1997.

[20]

S. Muggleton. Inductive logic programming. New Generation Computing, 8(4): 295-318, 1991.

Digital Library

[21]

J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, 1988.

Digital Library

[22]

G. Piatetsky-Shapiro and W. Frawley, editors. Knowledge Discovery in Databases. MIT Press, Cambridge, MA, 1991.

Digital Library

[23]

D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, San Francisco, CA, 1999.

Digital Library

[24]

J. K. Quinlan. Induction of decision trees. Machine Learning, 1: 81-106, 1986.

Digital Library

[25]

P. Taylor. Statistical methods. In M. Berthold and D.J. Hand, editors, Intelligent Data Analysis: An Introduction, pages 67-127. Springer, Berlin, 1999.

[26]

S. Weiss and N. Indurkhya. Predictive Data Mining: A Practical Guide. Morgan Kaufmann, San Francisco, CA, 1997.

Digital Library

[27]

I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA, 1999.

Digital Library

Cited By

Motomura SOhshima MZhong N(2013)Peculiarity Oriented EEG Data Stream MiningProceedings of the International Conference on Brain and Health Informatics - Volume 821110.1007/978-3-319-02753-1_15(147-157)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-319-02753-1_15
Hoonakker FLachiche NVarnek AWagner A(2010)A representation to apply usual data mining techniques to chemical reactionsProceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II10.5555/1945847.1945887(318-326)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.5555/1945847.1945887
Ohshima MZhong NYao YLiu C(2007)Relational peculiarity-oriented miningData Mining and Knowledge Discovery10.1007/s10618-006-0046-615:2(249-273)Online publication date: 1-Oct-2007
https://dl.acm.org/doi/10.1007/s10618-006-0046-6
Show More Cited By

Index Terms

Data mining in a nutshell
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Relational database model
    2. Query languages
      1. Relational database query languages
  2. Information systems applications

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Information & Contributors

Information

Published In

cover image Guide books

Relational Data Mining

October 2001

398 pages

ISBN:3540422897

Editor:
Saĕso Dĕzeroski
Joĕzef Stefan Institute, Ljubljana, Slovenia

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 October 2001

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Cited By

Motomura SOhshima MZhong N(2013)Peculiarity Oriented EEG Data Stream MiningProceedings of the International Conference on Brain and Health Informatics - Volume 821110.1007/978-3-319-02753-1_15(147-157)Online publication date: 29-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-319-02753-1_15
Hoonakker FLachiche NVarnek AWagner A(2010)A representation to apply usual data mining techniques to chemical reactionsProceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II10.5555/1945847.1945887(318-326)Online publication date: 1-Jun-2010
https://dl.acm.org/doi/10.5555/1945847.1945887
Ohshima MZhong NYao YLiu C(2007)Relational peculiarity-oriented miningData Mining and Knowledge Discovery10.1007/s10618-006-0046-615:2(249-273)Online publication date: 1-Oct-2007
https://dl.acm.org/doi/10.1007/s10618-006-0046-6
Zhang CZhang ZCao L(2005)Agents and data miningProceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining10.1007/11492870_5(50-61)Online publication date: 6-Jun-2005
https://dl.acm.org/doi/10.1007/11492870_5

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