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A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering

Shivani Walia1 , P S Mann2

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-1 , Page no. 30-39, Jan-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i1.3039

Online published on Jan 31, 2020

Copyright © Shivani Walia, P S Mann . 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: Shivani Walia, P S Mann, “A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.30-39, 2020.

MLA Style Citation: Shivani Walia, P S Mann "A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering." International Journal of Computer Sciences and Engineering 8.1 (2020): 30-39.

APA Style Citation: Shivani Walia, P S Mann, (2020). A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering. International Journal of Computer Sciences and Engineering, 8(1), 30-39.

BibTex Style Citation:
@article{Walia_2020,
author = {Shivani Walia, P S Mann},
title = {A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2020},
volume = {8},
Issue = {1},
month = {1},
year = {2020},
issn = {2347-2693},
pages = {30-39},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4992},
doi = {https://doi.org/10.26438/ijcse/v8i1.3039}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i1.3039}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4992
TI - A Hybrid Classification Algorithm Using Landmark Based Spectral Clustering
T2 - International Journal of Computer Sciences and Engineering
AU - Shivani Walia, P S Mann
PY - 2020
DA - 2020/01/31
PB - IJCSE, Indore, INDIA
SP - 30-39
IS - 1
VL - 8
SN - 2347-2693
ER -

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Abstract

Landmark-based Spectral Clustering (LSC) is used for large scale spectral clustering. The basic idea of the our approach is designing an efficient way for graph construction. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure like distance functions. k-NN is a type of instance-based learning, or lazy learning. In this field, the CRF approach is relatively novel and considered a prominent choice as compared to other methods. However, a lot of scope for further enhancement of the CRF(Conditional Random Field) with Knn optimization problem. The Performance of CRF-Knn has shown quite significant resultsand using different datasets in this paper.The proposed technique improves the selection process using KNN algorithm. The results obtained show that the CRF found to be better than that of LSC in terms of Accuracy, time, recall and precision.

Key-Words / Index Term

Landmark-based Spectral clustering, K nearest neighbors, CRF, Accuracy, time, precision and recall

References

[1] Smita,Priti Sharma,”Use of Data Mining in Various Field: A Survey Paper”,IOSR Journal of Computer Engineering (IOSR-JCE)7Volume 16,pp.18-21,2014.
[2] Dhara Patel, Ruchi Modi , Ketan Sarvakar “A Comparative Study of Clustering Data Mining: Techniques and Research Challenges”, IJLTEMAS, Volume 3, 2014.
[3] Amit Tate,Bajrangsingh Rajpurohit, Jayanand Pawar,UjwalaGavhance,”Comparative Analysis used for Disease Prediction in Data Mining”,International Journalof Engineering and Techniques, Volume 2, 2016.
[4] Thair Nu Phyu ,”Survey of Classification Techniques in Data Mining “,International MultiConference of Engineers and Computer Scientists,Volume 1, 2009.
[5] Neha Midha,Vikram Singh,“A Survey on Classification Techniques in Data Mining”, International Journal of Computer Science & Management Studies, Volume 16, 2015
[6] Deng, Zhenyun, Xiaoshu Zhu, Debo Cheng, Ming Zong,Shichao Zhang. "Efficient kNN classification algorithm for big data.",proceedings Neurocomputing 195 ,2016.
[7] Iyer, S. Jeyalatha,R. Sumbaly, “Diagnosis of Diabetes using Classification Mining Techniques”, IJDKP, Volume 5, pp. 1-14, 2015.
[8] Jasmina novakovic, “Experimental Study Of Using The K-Nearest Neighbour Classifier With Filter Methods,” in computer science and technology at varna, Bulgaria.
[9] Imandoust, Sadegh Bafandeh, Mohammad Bolandraftar. "Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background." International Journal of Engineering Research and Applications 3.5 2013.
[10] Amir ali, “An Intuitive Guide of K-Nearest Neighbor with Practical”, Wavy AI Research Foundation in k-Nearest Neighbor.
[11] Arslan, Farrukh. "An Efficient K-Nearest Neighbor Algorithm to Determine SOP File System." ,2018.
[12] Shufeng chen , “K-Nearest Neighbor Algorithm Optimization in Text Categorization” IOP conference series, earth and environment sciences.
[13] Yun-lei cui , “A KNN Research Paper Classification Method Based on Shared Nearest Neighbor” , Proceedings of NTCIR-8 Workshop Meeting,Tokyo, Japan,2010.
[14] Khalid Alkhatib, “Stock price prediction using KNN algorithm” in International Journal of Business, Humanities and Technology Volume 3,2013.
[15] H.P. Channe ,Sayali.D.Jadhav ,“ Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques”, International Journal of Science and Research (IJSR),Volume 5, 2016
[16] Ramana, Bendi Venkata, M. Surendra Prasad Babu, N. B. Venkateswarlu. "A critical study of selected classification algorithms for liver disease diagnosis." International Journal of Database Management Systems , Volume 3,2011.
[17] Rajkumar ,G. S. Reena, “Diagnosis of Heart Disease Using Datamining Algorithm,” Global Journal of Computer Science and Technology, Volume 10, 2010.
[18] Fadl Mutaher Ba-Alwi ,Houzifa M. Hintaya,” Comparative Study for Analysis the Prognostic in Hepatitis Data: Data Mining Approach”, International Journal of Scientific & Enginerring Research, Volume 4, 2013..
[19] Rohit Arora,Suman“ comparative Analysis of Classification Algorithms on Different using WEKA”, International Journal of Computer Applications ,Volume 54,2012.
[20] Samir Kumar Sarangi , Dr. Vivek Jaglan, Yajnaseni Dash ,“ A Review of Clustering and Classification Techniques in Data Mining”,International Journal of Engineering, Business and Enterprise Applications,2015.
[21] Marie Fernandes,” Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering Volume-5, Issue-1, pp.19-23, 2017(E-ISSN: 2320-7639).
[22] Himanshi , Komal Kumar Bhatia,” Prediction Model for Under-Graduating Student’s Salary Using Data Mining Techniques”, International Journal Scentific Research in Network Security and Communication, Volume-6, Issue-2, April 2018(ISSN: 2321-3256).