Mining method for communication alarm association rule based on maximal frequent item set

Abstract

The invention provides a method for building an alarm association rule mining system based on a maximal frequent item set DM (Data Mining) and the realization of that. Three different mining ways for single equipment, cognate equipment and linked equipment are designed aiming to different types of equipment in a communication network, and according to the mining range, the DM range can be positioned at specific city level or communication equipment manufacturer level; after the mining way is confirmed, the alarm association time window, the sliding step, etc are selected to acquire an alarm affair set assembly; and alarm association result mining is performed by utilizing the maximal frequent item set mining algorism after a user inputs the minimum support, and mining result treatment and display are performed according to different mining ways. Through the mentioned steps, the alarm association rule needed by the user can be found out from plenty of alarm data. The method has broad application prospect and favorable utility value.

Description

technical field [0001] The invention relates to a communication alarm association rule mining method based on the maximum frequent item set, in particular to a method. The invention belongs to the field of communication network alarm monitoring, in particular relates to communication alarm association analysis based on data mining technology. Background technique [0002] A large amount of historical alarm information is stored in the network alarm database, which contains a lot of useful information reflecting the network operation status and the root cause of the fault, which can be used to improve network fault management. The analysis of communication alarm association rules based on data mining technology can reveal meaningful knowledge and alarm correlation hidden in massive original alarm data by analyzing alarm transaction data and mining alarm association rules, and explain the network more comprehensively and systematically. Faults and performance problems enable ...

AI Technical Summary

Problems solved by technology

However, the requirements for the hardware system are very high, requiring long-term calculations and taking up a large amount of storage space

Moreover, many of the mining results obtained are invalid for practical applications, and even some of the results are redundant, which cannot realize the efficient mining of association rules, and is not suitable for the sorting out of alarm association rules in the later stage and the actual alarm monitoring and fault location.

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