Art. 01 – Vol. 22 – No. 4 – 2012

Data mining and Knowledge Discovery – Issues,
Objectives and Strategies

Cornel Lepădatu
The Romanian Academy
Romanian Academy Library

Abstract: Data mining and knowledge discovery denote a set of methods and algorithms for exploration and analysis of (often) large volumes of data aiming to infer rules, associations, unknown trends, specific structures so that useful information may be returned in a concise form for supporting decisions. Despite its fast-paced development the data mining is still vaguely defined and lacks an integrated approach. This situation causes difficulties in teaching, learning, research and application. The success of a project in any field of activity of contemporary organizations is often compromised by the general propensity to develop solutions before identifying problems and formulating statements. The article focuses on several important aspects such as the nature and quality of data used in the application of data mining, the most commonly used methods, the choice of the main objectives, problem formulation that should be adequately addressed in the context of data mining common strategies.

Keywords: data mining goals, data mining problems, data mining process, data mining strategies, data mining technology.


  1. BACCINI, A.; BESSE, P.: Data mining / Exploration Statistique. Toulouse: INSA, 2010, 111 p.
  2. BESSE, P.: Apprentissage Statistique & Data mining. Toulouse: INSA, 2009, 124 p.
  3. ENĂCHESCU, D.: Data Mining – metode şi aplicaţii. Bucureşti: Editura Academiei Române, 2009, 277 p.

View full article

  1. FILIP, F. G.: Decizie asistată de calculator: decizii, decidenţi – metode de bază şi instrumente informatice asociate, Ed. a 2-a, rev. Bucureşti: Editura Tehnică, 2005, 376 p.
  2. FILIP, F. G.: Sisteme suport pentru decizii, Ed. a 2-a, rev. Bucureşti: Ed. Tehnică, 2007, 364 p.
  3. GORUNESCU, F.: Data Mining, Concepts, Models and Techniques, Springer- Heidelberg, series Intelligent Systems Reference Library, 2011, 372 p.
  4. HASTIE, T.; TIBSHIRANI, R.; FRIEDMAN, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer-Verlag, Springer Series in Statistics, 2008, 763 p.
  5. MĂRGINEAN, N.: Sisteme inteligente pentru asistarea deciziilor. Editura Risoprint, Cluj-Napoca, 2006, 239 p.
  6. PENG, Y.; KOU, G.; SHI, Y.; CHEN, Z.: A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology & Decision Making, Vol. 7, No. 4, 2008, pp. 639-682.
  7. TAN, P-N.; STEINBACH, M.; KUMAR, V.:Introduction to Data Mining. Addison-Wesley, 2006, 769 p.
  8. TUFFERY, S.: Data mining et statistique décisionnelle, 3ème Edition. Editions TECHNIP, 2010, 705 p.
  9. WU, X.; KUMAR, V. (ed.): The Top Ten Algorithms in Data Mining. Chapman & Hall / CRC DMKD Series, 2009, 232 p.
  10. YU, P-S.; HAH, J.; FALOUSTOS, C. (ed.): Link Mining: Models, Algorithms, and Applications. Springer, 2010, 586 p.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.