Study of Data Mining Techniques and Its Types

Authors

  • Alok Kumar Awasthi  Department of Computer Science and Engineering, Saraswati Higher Education and Technical College of Engineering, Varanasi, Uttar Pradesh, India

Keywords:

Data Mining, Classification, Clustering, Decision Tree, Associative Rule

Abstract

Data mining, classification, clustering, decision tree, Associative rule embedded processor technology moving towards faster and smaller processors and systems on a chip, it becomes increasingly difficult to accurately evaluate real time performance. This research describes an evaluation method using an embedded architecture software emulator that models the Motorola M-CORE processor architecture. This emulator is used to evaluate and compare the real-time performance of a public-domain experimental Real-Time Operating System (RTOS) against a bare-bones multi-rate task scheduler. The results of the experiment, as shown in arrival time JITTER, response-time DELAY, and CPU BREAKDOWN figures, show the trade-offs between job load, job frequency, and kernel overhead. This research suggests full-system software emulation to be a valid method of evaluating embedded systems’ behavior and real-time performance.

References

  1. Cabena, Peter; Hadjnian, Pablo; Stadler, Rolf; Verhees, Jaap; Zanasi, Alessandro (1997); Discovering Data Mining: From Concept to Implementation, Prentice Hall, ISBN 0-13-743980-6
  2. M.S. Chen, J. Han, P.S. Yu (1996) "Data mining: an overview from a database perspective". Knowledge and data Engineering, IEEE Transactions on 8 (6), 866–883
  3. Feldman, Ronen; Sanger, James (2007); The Text Mining Handbook, Cambridge University Press, ISBN 978-0-521-83657-9
  4. Guo, Yike; and Grossman, Robert (editors) (1999); High Performance Data Mining: Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers
  5. Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan kaufmann, 2006.
  6. Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome (2001); The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, ISBN 0-387-95284-5
  7. Liu, Bing (2007, 2011); Web Data Mining: Exploring Hyperlinks, Contents and Usage Data.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-06-30

Issue

Volume 5, Issue 3, May-June-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Alok Kumar Awasthi, " Study of Data Mining Techniques and Its Types" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 3, pp.529-533, May-June-2019.