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CLC number: TP312

On-line Access: 2018-08-06

Received: 2017-08-04

Revision Accepted: 2017-12-03

Crosschecked: 2018-06-08

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Rabia Irfan


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.6 P.763-782


TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Author(s):  Rabia Irfan, Sharifullah Khan, Kashif Rajpoot, Ali Mustafa Qamar

Affiliation(s):  School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan; more

Corresponding email(s):   12phdrirfan@seecs.edu.pk

Key Words:  Taxonomy, Clustering algorithms, Information science, Knowledge management, Machine learning

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.

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DOI - 10.1631/FITEE.1700517

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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