On Reducing Space Complexity of Fuzzy Neighborhood Based Clustering Algorithms

被引：0

作者：

Atilgan, Can ^{[1
]}

Nasibov, Efendi ^{[1
]}

机构：

[1] Dokuz Eylul Univ, Dept Comp Sci, Izmir, Turkey

来源：

2017 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK) | 2017年

关键词：

lustering; Fuzzy neighborhood; Fuzzy joint points;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Using fuzzy neighborhood relations in density-based clustering, like in Fuzzy Joint Points (FJP) algorithm, yields more robust and autonomous algorithms. 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 transformed HP algorithm with low space complexity is proposed.

引用

页码：577 / 579

页数：3

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