Semi-Supervised Evolving Approach for Data Streams Classification Based on Online Gustafson-Kessel Algorithm

被引：0
|
作者：
Gorbunov, I. V. [1 ]
Kalmykov, M. O. [1 ]
Rasskazov, E. V. [1 ]
Yankovskaya, A. E. [2 ]
机构：
[1] Tomsk State Univ Control Syst & Radioelect, Dept Complex Informat Secur, Tomsk, Russia
[2] Tomsk State Univ Control Syst & Radioelect, Natl Inst Tomsk Polytech Univ, Natl Res Tomsk State Univ, Tomsk State Univ Architecture & Bldg, Tomsk, Russia
来源：
2017 11TH IEEE INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT 2017) | 2017年
基金：
俄罗斯基础研究基金会;
关键词：
EVOLVING SYSTEMS; CLASSIFICATION; GUSTAFSON-KESSEL ALGORITHM; SEMI-SUPERVISED; DATA STREAMS;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The purpose of the article is to present the approach to data streams qualification in mode that is close to semi-supervised mode. The given approach combines modified online Gustafson-Kessel algorithm for work in mode of data classification. In the approach, there are used such steps as update of clusters, merging of clusters and deleting of unused for a longer time clusters. The benchmark of the accuracy of the suggested approach based on datasets from information repository keel.es is represented. In the conclusion, the recommendations of the suggested approach are given.
引用
收藏
页码：206 / 209
页数：4
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