Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Abstract

Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.

Association Analysis in Java Ateka for Stationery Sales Promotion Using the FP-Growth Algorithm

“…The results of the study are the average recovery time of the proposed customer service data analysis platform operating anomaly monitoring method is 5.239 seconds, and the average platform operating anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform is integrated with FP improved-Growth algorithm is operating abnormally Monitoring method performs better. [7]…”

Research on improved algorithm of FP-growth based on data strong association

“…Hong Van [4] et al proposed to study the original feature set in the database, analyze the characteristics of the original data, improve the original feature set, and then use FP-Growth for data mining, which finally proved to improve the Changsheng tong [5] proposed an FP-Growth mining analysis method based on historical operation and maintenance data and fault data to improve the effectiveness of association rules. Jing Yang [6] et al Proposed an FP-Growth algorithm with threshold setting to reduce errors, which improved the stability and accuracy of the detection system. Yang X et al Proposed an algorithm based on FP-Growth to reduce the infrequently used data by improving the decomposition database, and proved through experiments that the improved FP-Growth algorithm.…”

“…(5) Make use of frequent two items set A1 of historical data set A, and delete the item in real-time data set B if it does not contain any frequent two items set in A1(6) The SDA-FP-Growth algorithm generates FP-Tree (7) finally obtains frequent item set K of real-time dataset B3.3 Algorithm animationThis paper chooses database A and dataset B with 10 items as examples to demonstrate the algorithm. As shown in Table4 and…”