两种聚类算法的比较及其应用
Comparison and Application of Two Clustering Algorithms
成晓敏1
作者信息
- 1. 湖北职业技术学院,湖北 孝感 432000
- 折叠
摘要
聚类分析是一种非常重要的数据分析和数据处理方法,它在经济学、统计学、医学、生物学以及机器学习等领域都有广泛的应用.聚类分析在大数据时代是一个非常活跃的研究分支,能够有效得到各类隐藏信息和分类结果,从而进行优化与决策.目前已经研发出许多聚类算法,具体可分为基于划分的聚类、层次聚类、基于密度的聚类、基于图的聚类以及基于模型的聚类.主要对K-means聚类算法以及DBSCAN密度聚类算法进行详细的理论介绍,并将这两种算法用于实例分析进行比较得到各自的优缺点以及对未来研究工作的具体展望.
Abstract
Cluster analysis is a very important data analysis and data processing method.It has a wide range of applications in the fields of economics,statistics,medicine,biology and machine learning.Cluster analysis is a very active research branch in the era of big data,which can effectively obtain all kinds of hidden information and classification results for optimizing and making decisions.At present,many clustering algorithms have been devel-oped,which can be divided into partition-based clustering,hierarchical clustering,density-based clustering,graph-based clustering and model-based clustering.This paper mainly introduces K-means clustering algo-rithm and DBSCAN density clustering algorithm in details and compares these two algorithms for example analysis to obtain their respective advantages and disadvantages and specific prospects for future research work.
关键词
K-means算法/DBSCAN算法/数据挖掘/聚类分析Key words
K-means algorithm/DBSCAN algorithm/data mining/cluster analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据