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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


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:
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 = {},
doi = {}
publisher = {IJCSE, Indore, INDIA},

RIS Style Citation:
DO = {}
UR -
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
SP - 30-39
IS - 1
VL - 8
SN - 2347-2693
ER -

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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


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