Design of Log Analysis System Based on Multi-Object Clustering Algorithm

With the rapid development of the information age and the continuous expansion of data, all walks of life have begun to collect and analyze massive amounts of data to extract information that is valuable to them. Clustering technology in data mining is an important means of data analysis. Because different data sets have different distribution characteristics, traditional single-object clustering cannot adapt to the effective processing of different data sets, so multi-object clustering has gradually become a research hotspot. The purpose of this paper is the design of log analysis system based on a multi-objective clustering algorithm. This article first determines the main goals of the log analysis system and performs a detailed analysis of the system's demanding functional analysis and demanding non-functionality respectively. The log analysis system is mainly divided into five modules for the detailed design of the log analysis system. After the system design is completed, various functional modules are completed through corresponding technologies, the entire log system is tested for attacks, and the system is further improved. Finally, the system can completely analyze the attack type through log analysis and carry out relevant early warnings to meet the needs of users. When the data set is 1.8MB, the convergence time of the K-medoid algorithm is 3678.49, and the convergence time of the algorithm in this paper is 2536.42. When the data reaches 165MB, the convergence time of the algorithm in this paper is 4326.28, and the convergence time of the K-medoid algorithm is 8184. This shows that as the scale of data continues to increase, the processing time difference changes and the processing speed of this algorithm has obvious advantages.