Data Mining Application for Finding Patterns: Survey of Large Data Research Tools

Aive Islam; Tamzidl Amin

doi:doi:10.11648/j.ajnna.20170302.11

| Peer-Reviewed

Data Mining Application for Finding Patterns: Survey of Large Data Research Tools

Aive Islam, Tamzidl Amin

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 2)

Received: 1 November 2017 Accepted: 21 November 2017 Published: 5 December 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.

Published in	American Journal of Neural Networks and Applications (Volume 3, Issue 2)
DOI	10.11648/j.ajnna.20170302.11
Page(s)	14-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Neural Network, Decision Trees, Rule Induction Technique, Association Rules, Clustering, K-means

References

[1]	BalajiPadmanabhan and Alexander Tuzhilin, Institute for Operation Research and Management Science: 2011, On the use of optimisation for data mining:Theorotical Interaction and eCRM opportunities.
[2]	Shakil Ahmed, FransCoenen, Paul Leng, Knowledge Information System: 2006, Tree based partitioning of data for association rule mining.
[3]	RachidAnane, Computer and the humanities: 2001, Data mining and serial documents.
[4]	Anthony Danna, Oscar H. Gandy, Journal of business ethics: 2002, All that glitters is not gold: Digging Beneath the surface of data mining.
[5]	Peppers, D., & Rogers, M. (1993). The new marketing paradigm: one-to-one. American Advertising, 9(4), 93-102.
[6]	Leonid Churilov. AdylBagirov, Daniel Schwarta, Kate Smith, Michael Dally, Journal of management information system: 2005, Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules: application to prostate cancer patients
[7]	Fürnkranz, J., &Widmer, G. (1994). Incremental reduced error pruning. In Proceedings of the 11th International Conference on Machine Learning (ML-94) (pp. 70-77). Morgan Kaufmann.
[8]	Quinlan, J. R., & Cameron-Jones, R. M. (1993, April). FOIL: A midterm report. In European conference on machine learning (pp. 1-20). Springer, Berlin, Heidelberg.
[9]	Perkowitz, M., & Etzioni, O. (1998, July). Adaptive web sites: Automatically synthesizing web pages. In AAAI/IAAI (pp. 727-732).
[10]	Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
[11]	Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial intelligence review, 13(5-6), 393-408.
[12]	Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting Your Customers: Who-Are They and What Will They Do Next?. Management science, 33(1), 1-24.
[13]	Blattberg, R. C., & Deighton, J. (1991). Interactive marketing: Exploiting the age of addressability. Sloan management review, 33(1), 5.
[14]	Dreze, X., & Bonfrer, A. (2002). Lifetime Value Maximization through Optimal Communication Timing.
[15]	Mani, I., & Maybury, M. T. (Eds.). (1999). Advances in automatic text summarization (Vol. 293). Cambridge, MA: MIT press.
[16]	Theusinger, C., & Huber, K. P. (2000, August). Analyzing the footsteps of your customers. In Proc. of the Sixth ACM SIGKDD Internat. Conf. on Web KDD 2000 (pp. 44-52).

Cite This Article

Plain Text BibTeX RIS

APA Style

Aive Islam, Tamzidl Amin. (2017). Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. American Journal of Neural Networks and Applications, 3(2), 14-21. https://doi.org/10.11648/j.ajnna.20170302.11

Copy | Download

ACS Style

Aive Islam; Tamzidl Amin. Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. Am. J. Neural Netw. Appl. 2017, 3(2), 14-21. doi: 10.11648/j.ajnna.20170302.11

Copy | Download

AMA Style

Aive Islam, Tamzidl Amin. Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. Am J Neural Netw Appl. 2017;3(2):14-21. doi: 10.11648/j.ajnna.20170302.11

Copy | Download

@article{10.11648/j.ajnna.20170302.11,
  author = {Aive Islam and Tamzidl Amin},
  title = {Data Mining Application for Finding Patterns: Survey of Large Data Research Tools},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {2},
  pages = {14-21},
  doi = {10.11648/j.ajnna.20170302.11},
  url = {https://doi.org/10.11648/j.ajnna.20170302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170302.11},
  abstract = {Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Data Mining Application for Finding Patterns: Survey of Large Data Research Tools
AU  - Aive Islam
AU  - Tamzidl Amin
Y1  - 2017/12/05
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170302.11
DO  - 10.11648/j.ajnna.20170302.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 14
EP  - 21
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170302.11
AB  - Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.
VL  - 3
IS  - 2
ER  -

Copy | Download

Author Information

Aive Islam

Department of Computer Science & Engineering, United International University, Dhaka, Bangladesh
Tamzidl Amin

Department of Computer Science & Engineering, United International University, Dhaka, Bangladesh

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Aive Islam, Tamzidl Amin. (2017). Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. American Journal of Neural Networks and Applications, 3(2), 14-21. https://doi.org/10.11648/j.ajnna.20170302.11

Copy | Download

ACS Style

Aive Islam; Tamzidl Amin. Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. Am. J. Neural Netw. Appl. 2017, 3(2), 14-21. doi: 10.11648/j.ajnna.20170302.11

Copy | Download

AMA Style

Aive Islam, Tamzidl Amin. Data Mining Application for Finding Patterns: Survey of Large Data Research Tools. Am J Neural Netw Appl. 2017;3(2):14-21. doi: 10.11648/j.ajnna.20170302.11

Copy | Download

@article{10.11648/j.ajnna.20170302.11,
  author = {Aive Islam and Tamzidl Amin},
  title = {Data Mining Application for Finding Patterns: Survey of Large Data Research Tools},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {2},
  pages = {14-21},
  doi = {10.11648/j.ajnna.20170302.11},
  url = {https://doi.org/10.11648/j.ajnna.20170302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170302.11},
  abstract = {Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Data Mining Application for Finding Patterns: Survey of Large Data Research Tools
AU  - Aive Islam
AU  - Tamzidl Amin
Y1  - 2017/12/05
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170302.11
DO  - 10.11648/j.ajnna.20170302.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 14
EP  - 21
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170302.11
AB  - Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.
VL  - 3
IS  - 2
ER  -

Copy | Download