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一种基于相互邻居的集群方法及其医学应用

Jun Chen¹, Xinzhong Zhu², Huawen Liu³

¹Zhejiang Industry Polytechnic College, Shaoxing 312000, PR China.

Computers in biology and medicine

|October 20, 2023

概括

这项研究引入了一种新的集群算法,可以克服K-means的限制. 该方法有效地识别任意形状的集群使用相互邻居和路径距离,改进各种应用中的数据分析.

科学领域:

数据科学数据科学数据科学
机器学习机器学习
计算统计学计算统计学

背景情况:

聚类分析对于现实世界的应用至关重要.
由于其简单性,K-means是一种流行的集群技术.
K-意味着性能对初始中心敏感,并与多重集群作斗争.

研究的目的:

提出一种能够识别任意形状集群的新型集群算法.
解决传统K媒介的局限性,包括对初始中心的敏感性和无法检测多重结构的局限性.

主要方法:

使用相互邻居来估计数据对象密度.
识别高密度的代表物体.
引入路径距离,从最小跨度树获得,以测量多重结构的相似性.
通过将代表性对象和基于路径的距离用于初始中心和相似度测量来改进K-平均值.
根据邻里信息为非代表性对象分配集群标签.

主要成果:

拟议的集群方法有效地识别了任意形状的集群.
在合成数据的实验中,与最先进的集群方法相比,表现出更高的性能.
成功应用于医疗场景,识别不同的疾病类型.

结论:

这种新的集群算法克服了在识别复杂集群形状时的K-means限制.
该方法为密度估计和聚类相似性测量提供了强大的方法.
该算法显示了需要识别任意形状集群的应用程序的巨大潜力,包括医疗数据分析.

关键词:

集群分析分析集群分析 K-意味着K的意思.机器学习是机器学习.数据多种多样的数据.医学疾病疾病疾病医学互惠的邻居是共同的邻居.

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