Analysis of K-means and K-DBSCAN Commonly Used in Data Mining
Songfeng Sun, Kaibo Lei, Zunshun Xu, Wubin Jing, Guang Sun · 2023
In today’s era of big data, the amount of potential information behind the data is huge, So how to dig out the potential information in the data more scientifically and efficiently is bound to be a major research hotspot in the world. To solve this problem, we need data mining. therefore, This paper analyzes two algorithms K-means and k-DBSCAN commonly used in data mining, In the second section, we describe the principle and specific implementation steps of the two algorithms, And in the third section, We contrast the two, enumerate the merits and drawbacks of the two algorithms., and examine the differences between the two, and list three cases to analyze the applicability of each in three cases. A lot of complicated data sets may be processed efficiently using clustering. without class markers, which are widely used in the financial industry, biology, astronomy and other fields And K-means and k-DBSCAN are the main algorithms in clustering. By analyzing and comparing the two algorithms, we can know which algorithm is more appropriate in which scenario.