A Space Efficient Minimum Spanning Tree Approach to the Fuzzy Joint Points Clustering Algorithm

被引：8
作者：
Atilgan, Can [1 ]
Nasibov, Efendi N. [1 ,2 ]
机构：
[1] Dokuz Eylul Univ, Dept Comp Sci, TR-35390 Izmir, Turkey
[2] Azerbaijan Natl Acad Sci, Inst Control Syst, AZ-1141 Baku, Azerbaijan
来源：
IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2019年 / 27卷 / 06期
关键词：
Clustering; fuzzy joint points (FJPs); fuzzy neighborhood; space efficiency;
D O I：
10.1109/TFUZZ.2018.2879465
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The fuzzy joint points (FJPs) method is a neighborhood-based clustering method that uses a fuzzy neighborhood relation and eliminates the need for a parameter. Even though the fuzzy neighborhood-based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a minimum spanning tree based reduced space FJP algorithm is proposed. The computational experiments show that the reduced space algorithm enables the method to be used for much larger data sets.
引用
收藏
页码：1317 / 1322
页数：6
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