Paper Info
Title
Unsupervised Ranking Of Numerical Observations Based On Magnetic Properties And Correlation Coefficient
Abstract
This paper addresses a novel unsupervised algorithm to rank numerical observations which is important in many applications in computer science, especially in information retrieval (IR). The proposed algorithm shows how correlation coefficients between attribute values and the concept of magnetic properties can be explored to rank multi-attribute numerical objects. One of the main reasons of using correlation coefficients between attribute values and the concept of magnetic properties is that they are easy to compute and interpret. Our proposed Unsupervised Ranking using Magnetic properties and Correlation coefficient (URMC) algorithm can use some or all the numerical attributes of objects and can also handle objects with missing attribute values. The proposed algorithm overcomes a major limitation of the state-of-the-art technique while achieving excellent results.
Year
DOI
Venue
2019
10.24251/hicss.2019.139
PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES
Field
DocType
Citations 
Correlation coefficient,Data mining,Ranking,Computer science,Management science
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Khalid Alattas122.38
Aminul Islam232831.16
Ashok Kumar310215.47
Magdy Bayoumi419036.91