Comparison and Application of Two Clustering Algorithms
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.
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