CLC number: TP312
On-line Access: 2018-08-06
Received: 2017-08-04
Revision Accepted: 2017-12-03
Crosschecked: 2018-06-08
Cited: 0
Clicked: 6858
Rabia Irfan, Sharifullah Khan, Kashif Rajpoot, Ali Mustafa Qamar. TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(6): 763-782.
@article{title="TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data",
author="Rabia Irfan, Sharifullah Khan, Kashif Rajpoot, Ali Mustafa Qamar",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="6",
pages="763-782",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700517"
}
%0 Journal Article
%T TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data
%A Rabia Irfan
%A Sharifullah Khan
%A Kashif Rajpoot
%A Ali Mustafa Qamar
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 6
%P 763-782
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700517
TY - JOUR
T1 - TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data
A1 - Rabia Irfan
A1 - Sharifullah Khan
A1 - Kashif Rajpoot
A1 - Ali Mustafa Qamar
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 6
SP - 763
EP - 782
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700517
Abstract: taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.
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