Data Clustering by Nature-inspired Algorithms and Chaotic Maps

被引：0
|
作者：
Bejinariu, Silviu-Ioan [1 ]
Costin, Hariton [1 ,2 ]
Rotaru, Florin [1 ]
Luca, Ramona [1 ]
机构：
[1] Romanian Acad, Inst Comp Sci, Iasi Branch, Iasi, Romania
[2] Gr T Popa Univ Med & Pharm, Fac Med Bioengn, Iasi, Romania
来源：
2019 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB) | 2019年
关键词：
Nature-inspired algorithms; chaotic maps; clustering; classification;
D O I：
10.1109/ehb47216.2019.8970068
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
A data clustering procedure based on the usage of nature inspired (NI) algorithms combined with chaotic sequences is proposed. Four NI algorithms: Particle Swarm Optimization (PSO), Multi Swarm Optimization (MSO), Cuckoo Search Algorithm (CSA) and Black Hole Algorithm (BHA) where combined with nine chaotic maps. The procedure was validated using a medical database and the results were compared to those obtained by the k-means algorithm and also other clustering methods proposed in literature. The chaotic versions of PSO and MSO offer near optimal results faster, but the chaotic version of CSA offers more precise solutions after increasing the number of iterations.
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页数：4
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