Performance Analysis of Quickreduct, Quick Relative Reduct Algorithm	and a New Proposed Algorithm

Performance Analysis of Quickreduct, Quick Relative Reduct Algorithm and a New Proposed Algorithm

Ashima Gawar

Contact

Prerna Mahajan

Contact

Guru Gobind Singh Indraprastha University

Performance Analysis of Quickreduct, Quick Relative Reduct Algorithm and a New Proposed Algorithm

Article Fingerprint

ReserarchID

CSTSDE553BW

Performance Analysis of Quickreduct, Quick Relative Reduct Algorithm and a New Proposed Algorithm Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Feature Selection is a process of selecting a subset of relevant features from a huge dataset that satisfy method dependent criteria and thus minimize the cardinality and ensure that the accuracy and precision is not affected ,hence approximating the original class distribution of data from a given set of selected features. Feature selection and feature extraction are the two problems that we face when we want to select the best and important attributes from a given dataset Feature selection is a step in data mining that is done prior to other steps and is found to be very useful and effective in removing unimportant attributes so that the storage efficiency and accuracy of the dataset can be increased. From a huge pool of data available we want to extract useful and relevant information. The problem is not the unavailability of data , it is the quality of data that we lack in.. We have Rough Sets Theory which is very useful in extracting relevant attributes and help to increase the importance of the information system we have. Rough set theory works on the principle of classifying similar objects into classes with respect to some features and those features may collectively and shortly be termed as reducts.

References

10 Cites in Article

Reference Format

G Jothi.,H Hannah Inbarani. (2012). Soft set based quick reduct approach for unsupervised feature selection.
Z Pawlak (1982). Rough sets.
C Velayutham,K Thangavel (2011). Unsupervised Quick Reduct Algorithm Using Rough Set Theory.
calculating discernibility functions which can be.
Jing Zhang,Jianmin Wang,Deyi Li,Huacan He,Jiaguang Sun (2003). A New Heuristic Reduct Algorithm Base on Rough Sets Theory.
N Suguna,Dr,K Thanushkodi (2010). A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization.
T Chandrasekhar,K Thangavel,E Elayaraja,E Sathishkumar (2012). Unsupervised gene expression data using enhanced clustering method.
R Slowinski,D Vanderpooten (2000). A generalized definition of rough approximations based on similarity.
Y Yiyu,Yan (2009). Discernibility Matrix Simpli_cation for Constructing Attribute Reducts Discernibility matrix simplication for constructing attribute reducts.
Y Yao,S Wong,T Lin (1997). A Review of Rough Set Models.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ashima Gawar. 2014. \u201cPerformance Analysis of Quickreduct, Quick Relative Reduct Algorithm and a New Proposed Algorithm\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C4): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 14 Issue C4
Pg. 1- 5

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Data mining feature extraction feature selection quick relative reduct quickreduct rough set

Submission ReceivedDecember 13, 2013
Peer Review Double Blind
Handling Editor
Accepted January 1, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

July 5, 2014

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Ashima Gawar, Prerna Mahajan (PhD/Dr. count: 0)

View Count (all-time): 290

Total Views (Real + Logic): 8689

Total Downloads (simulated): 2133

Publish Date: 2014 07, Sat

Monthly Totals (Real + Logic):

Month 1: 68 views
Month 2: 17 views
Month 3: 20 views
Month 4: 25 views
Month 5: 32 views
Month 6: 42 views
Month 7: 25 views
Month 8: 38 views
Month 9: 30 views
Month 10: 19 views
Month 11: 52 views
Month 12: 36 views
Month 13: 46 views
Month 14: 32 views
Month 15: 25 views
Month 16: 24 views
Month 17: 20 views
Month 18: 37 views
Month 19: 44 views
Month 20: 28 views
Month 21: 24 views
Month 22: 24 views
Month 23: 16 views
Month 24: 29 views
Month 25: 21 views
Month 26: 43 views
Month 27: 30 views
Month 28: 38 views
Month 29: 30 views
Month 30: 28 views
Month 31: 47 views
Month 32: 23 views
Month 33: 40 views
Month 34: 34 views
Month 35: 16 views
Month 36: 27 views
Month 37: 23 views
Month 38: 29 views
Month 39: 27 views
Month 40: 17 views
Month 41: 44 views
Month 42: 23 views
Month 43: 26 views
Month 44: 32 views
Month 45: 40 views
Month 46: 36 views
Month 47: 27 views
Month 48: 27 views
Month 49: 24 views
Month 50: 28 views
Month 51: 23 views
Month 52: 12 views
Month 53: 21 views
Month 54: 24 views
Month 55: 29 views
Month 56: 34 views
Month 57: 43 views
Month 58: 24 views
Month 59: 22 views
Month 60: 32 views
Month 61: 43 views
Month 62: 16 views
Month 63: 40 views
Month 64: 18 views
Month 65: 31 views
Month 66: 33 views
Month 67: 25 views
Month 68: 18 views
Month 69: 35 views
Month 70: 34 views
Month 71: 23 views
Month 72: 36 views
Month 73: 24 views
Month 74: 26 views
Month 75: 34 views
Month 76: 42 views
Month 77: 24 views
Month 78: 28 views
Month 79: 27 views
Month 80: 34 views
Month 81: 32 views
Month 82: 28 views
Month 83: 45 views
Month 84: 21 views
Month 85: 23 views
Month 86: 36 views
Month 87: 31 views
Month 88: 27 views
Month 89: 40 views
Month 90: 17 views
Month 91: 23 views
Month 92: 26 views
Month 93: 37 views
Month 94: 29 views
Month 95: 43 views
Month 96: 45 views
Month 97: 22 views
Month 98: 29 views
Month 99: 32 views
Month 100: 11 views
Month 101: 21 views
Month 102: 33 views
Month 103: 35 views
Month 104: 24 views
Month 105: 21 views
Month 106: 28 views
Month 107: 23 views
Month 108: 32 views
Month 109: 22 views
Month 110: 27 views
Month 111: 26 views
Month 112: 44 views
Month 113: 30 views
Month 114: 40 views
Month 115: 48 views
Month 116: 34 views
Month 117: 37 views
Month 118: 33 views
Month 119: 34 views
Month 120: 30 views
Month 121: 23 views
Month 122: 23 views
Month 123: 47 views
Month 124: 30 views
Month 125: 24 views
Month 126: 19 views
Month 127: 28 views
Month 128: 27 views
Month 129: 26 views
Month 130: 31 views
Month 131: 34 views
Month 132: 48 views
Month 133: 27 views
Month 134: 41 views
Month 135: 26 views
Month 136: 25 views
Month 137: 50 views
Month 138: 31 views
Month 139: 39 views
Month 140: 21 views
Month 141: 52 views
Month 142: 51 views

Total Views: 8689

Total Downloads: 2133

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]