TIE算法：一种用于处理演化数据的聚类分层分类法生成技术上层算法

Rabia IRFAN; Sharifullah KHAN; Kashif RAJPOOT; Ali Mustafa QAMAR

doi:10.1631/FITEE.1700517

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TIE算法：一种用于处理演化数据的聚类分层分类法生成技术上层算法

常规文章 | Updated：2022-05-19

- TIE算法：一种用于处理演化数据的聚类分层分类法生成技术上层算法
- TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data
- 信息与电子工程前沿（英文） 2018年19卷第6期页码：763-782
- Affiliations：
  
  School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
  School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
  Department of Computer Science, College of Computer, Qassim University, Al Mulaida, Buraydah 52344, Saudi Arabia
- Author bio：
  
  Rabia IRFAN, E-mail: 12phdrirfan@seecs.edu.pk
- Funds：
- DOI：10.1631/FITEE.1700517
  中图分类号： TP312
- 收稿：2017-08-04，
  
  修回：2018-;6-;8，
  
  纸质出版：2018-06
- Accepted：
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Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, 等. TIE算法：一种用于处理演化数据的聚类分层分类法生成技术上层算法[J]. 信息与电子工程前沿（英文）, 2018,19(6):763-782.

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, et al. 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.
Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, 等. TIE算法：一种用于处理演化数据的聚类分层分类法生成技术上层算法[J]. 信息与电子工程前沿（英文）, 2018,19(6):763-782. DOI： 10.1631/FITEE.1700517.

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, et al. 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. DOI： 10.1631/FITEE.1700517.

摘要

分类法可实现对大量数据的有效组织和访问。分类法是表示数据概念的一种方法，其需要通过不断演进来反映数据变化。现有分类法自动生成技术无法处理数据演化，因此，所生成的分类法不能真实反映数据。为反映数据演变，可从头对分类法进行再生，或根据数据变化随时对分类法进行增量演进。其中，前者的时间和资源成本较高。提出一种新颖的分类增量进化（TIE）算法，用于处理随时间演变的数据。TIE是一种现有聚类分层分类法生成技术的上层算法，它允许现有分类法增量地演进。在计算机领域的研究论文中对该算法进行了评估。结果表明，与从头再生分类法相比，随数据演化的分类法生成算法耗时非常短，且在单位时间下性能更佳。

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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