Survey on Data Mining Technique
Harmeet Kaur1 , Jasleen Kaur2
Section:Survey Paper, Product Type: Journal Paper
Volume-6 ,
Issue-8 , Page no. 915-920, Aug-2018
CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i8.915920
Online published on Aug 31, 2018
Copyright © Harmeet Kaur, Jasleen Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Harmeet Kaur, Jasleen Kaur, “Survey on Data Mining Technique,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.915-920, 2018.
MLA Style Citation: Harmeet Kaur, Jasleen Kaur "Survey on Data Mining Technique." International Journal of Computer Sciences and Engineering 6.8 (2018): 915-920.
APA Style Citation: Harmeet Kaur, Jasleen Kaur, (2018). Survey on Data Mining Technique. International Journal of Computer Sciences and Engineering, 6(8), 915-920.
BibTex Style Citation:
@article{Kaur_2018,
author = {Harmeet Kaur, Jasleen Kaur},
title = {Survey on Data Mining Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2018},
volume = {6},
Issue = {8},
month = {8},
year = {2018},
issn = {2347-2693},
pages = {915-920},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2795},
doi = {https://doi.org/10.26438/ijcse/v6i8.915920}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.915920}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2795
TI - Survey on Data Mining Technique
T2 - International Journal of Computer Sciences and Engineering
AU - Harmeet Kaur, Jasleen Kaur
PY - 2018
DA - 2018/08/31
PB - IJCSE, Indore, INDIA
SP - 915-920
IS - 8
VL - 6
SN - 2347-2693
ER -
VIEWS | XML | |
617 | 326 downloads | 285 downloads |
Abstract
Now a Day, Many Companies and organizations are to make a large volume of information. In the enterprise, choice producers access from all sources and types of collection methods. The information warehouse is used for the enterprise for enhancing the selection-making. In aggressive commercial enterprise international, the values of strategic statistics techniques along with these are actually identified. The enterprise surroundings, the pace isn`t the simplest key to competitiveness. To investigate the records, it needs the unique equipment are called facts mining things. This paper survey of the facts mining set of rules which include Clustering, Time series, Logistic Regression, Naïve Bayes and its programs within the exclusive areas.
Key-Words / Index Term
Data mining, Clustering, Time series, Logistic Regression, Naïve Bayes
References
[1] Sam Fletcher et al. “An anonymization technique using intersected decision trees” Journal of King Saud University – Computer and Information Sciences (2015) 27, 297–304.
[2] Ashish Kumar et al. “Implementation and Comparison of Decision Tree Based Algorithms”, International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 4, Special Issue May 2015.
[3] Yash Jain et al.“A Survey On Data Mining Techniques, Their Application And Future Scope", International Journal Of Engineering Sciences & Research Technology [Jain*, 4.(8): April 2015] ISSN: 2277-9655 (I2OR), Publication Impact Factor: 3.785
[4] Neha Khan et al. “Big Data Classification using Evolutionary Techniques: A Survey”,2015 IEEE International Conference on Engineering and Technology (ICETECH), 20th March 2015, Coimbatore, TN, India.
[5] Sonia Singh et al. “COMPARATIVE STUDY ID3, CART AND C4.5 DECISION TREE ALGORITHM: A SURVEY”, International Journal of Advanced Information Science and Technology (IJAIST), ISSN: 2319:2682 Vol.27, No.27, July 2014.
[6] Manpreet Singh et al. “Performance Analysis of Decision Trees", International Journal of Computer Applications (0975 – 8887) Volume 71– No.19, June 2013
[7] T.Miranda Lakshmi et al. “An Analysis On Performance Of Decision Tree Algorithms Using Student’s Qualitative Data”, I.J.Modern Education And Computer Science, 2013, 5, 18-27 Published Online June 2013 In MECS (Http://Www.Mecs-Press.Org/) DOI: 10.5815/Ijmecs.2013.05.03
[8] Nikita Jain, Vishal Srivastava, M. Tech. Scholar, Associate Professor, Arya College Of Engineering And IT, “Data Mining Techniques: A Survey Paper”, International Journal Of Research In Engineering And Technology Eisen: 2319-1163 | ISSN: 2321-7308.
[9] Shikha Chourasia, “Survey paper on improved methods of ID3 decision tree classification”,
[10] International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013 ISSN 2250-3153
[11] Mohd Afizi Mohd Shukran, Faculty of Science & Defense Technology, National Defense University of Malaysia (NDUM) "Artificial Bee Colony based Data Mining Algorithms for Classification Tasks", Canadian Center of Science and Education www.ccsenet.org/mas Modern Applied Science Vol. 5, No. 4; August 2011.
[12] Mrs. Bharati M. Ramageri, Lecturer Of Modern Institute Of Information Technology And Research, Department Of Computer Application, “Data Mining Techniques And Applications”, Indian Journal Of Computer Science And Engineering Vol. 1 No. 4 301-305.
[13] Fahad S. Abu-Mouti and Mohamed E. El-Hawary, “Overview of Artificial Bee Colony (ABC) Algorithm and Its Applications” IEEE, 2012.
[14] Er. Ankit Choubey And Dr. G. L. Prajapati, ” An Understanding Of Abc Algorithm And Its Applications” International Journal Of Current Engineering And Scientific Research (IJCESR), ISSN (Print): 2393-8374, (Online): 2394-0697, Volume-2, Issue-10, 2015.
[15] Alkin Yurtkuran and Erdal Emel, “An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch” Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 8085953,2016.
[16] Quande Qin, Shi Cheng, Qingyu Zhang, Li Li and Yuhui Shi, "Artificial Bee Colony Algorithm with Time-Varying Strategy" Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2015, Article ID 674595, 2015.
[17] Dhanya P Varghese & Tintu P B, "A SURVEY ON HEALTH DATA USING DATA MINING TECHNIQUES", International Research Journal of Engineering and Technology (JET), Volume: 02 Issue: 07, Oct-2015.
[18] Doron Shalvi & Nicholas DeClaris, “AN UNSUPERVISED NEURAL NETWORK APPROACH TO MEDICAL DATA MINING TECHNIQUES”, IEEE, 1998.
[19] Gustavo Santos-Garcia & Gonzalo Varela & Nuria Novoa & Marcelo F. Jimenez, “PREDICTION OF POSTOPERATIVE MORBIDITY AFTER LUNG RESECTION USING AN ARTIFICIAL NEURAL NETWORK ENSEMBLE”, Artificial Intelligence in Medicine 30:61–69, 2004.
[20] Hojin Moon & Hongshik Ahn & Ralph Kodell & Songjoon Baek & Chien- Ju Lin & James Chen, “ENSEMBLE METHODS FOR CLASSIFICATION OF PATIENTS FOR PERSONALIZED MEDICINE WITH HIGH-DIMENSIONAL DATA”. Artificial Intelligence in Medicine 41:197–207, 2007.
[21] I. Curiac & G. Vasile & O. Banias & C. Volosencu & A. Albu, “BAYESIAN NETWORK MODEL FOR DIAGNOSIS OF PSYCHIATRIC DISEASES”, Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, Cavtat, Croatia, 22-25 June-2009.
[22] Jeong-Yon Shim & Lei Xu, “MEDICAL DATA MINING MODEL FOR ORIENTAL MEDICINE VIA BYY BINARY INDEPENDENT FACTOR ANALYSIS”, IEEE.P1-4, 2003.
[23] K.Sharmila & Dr.S.A.Vethamanickam, “SURVEY ON DATA MINING ALGORITHM AND ITS APPLICATION IN HEALTHCARE SECTOR USING HADOOP PLATFORM”, International Journal of Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume: 05, Issue: 01, January-2015.
[24] Andrii Shalaginov, Lars Strande Grini, Katrin Franke (2016) "Understanding Neuro-Fuzzy on a class of multinomial malware detection problems, In Neural Networks (IJCNN)," 2016 International Joint Conference
[25] Huda, Shamsul, et al. "A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis." IEEE Access 4 (2016): 9145-9154.
[26] Azad, S., Fattah, S. A., & Shahnaz, C. (2017, November). “An automatic scheme for brain tumor region detection from 3D MRI data based on enhanced intensity variation.” In Region 10 Conference, TENCON 2017-2017 IEEE (pp. 1-6). IEEE.
[27] Ramani RG, Sivaselvi K. Classification of “Pathological Magnetic Resonance Images of Brain Using Data Mining Techniques. In Recent Trends and Challenges in Computational Models (ICRTCCM),” 2017 Second International Conference on 2017 Feb 3 (pp. 77-82). IEEE.