Application Of Data Mining In Telecommunication Network Alarm Correlation Research

Along with the development of the network technology.Netwok resource present heterogeneity and Dynamic. The function of Network Management increasinqly complexity. The traditional network management technology was already unable to satisfy to the large-scale complex network management need. Large-scale and complex structure of moden networks have a large number of alarms per day. Alarm is a harmful abnormal events. is usually monitors automatically failure. It offers definite information to network management personnel. As a result of a single fault may lead to a series of alarms, so not all of the alarm indicated that the cause of the malfunction..Fault management system want to accurate positioning netwok fault very difficult. Traditional network management system and network administrators must rely on their own limited experience and limited network management system fault diagnosis functions, location and recovery. However, expanding the network and the rapidly evolving circumstances, such knowledge has been unable to meet demand.At present, Scholars at home and abroad to carry out a lot of alarm network research, there are a variety of methods have been applied to a network failure in the alarm correlation analysis. the data mining methods have been extensively studied and applied.In this paper, first ,summarize the concept of data mining, functional, basic processes and so on. Second, introduce the basic structure of the telecommunication network as well as the characteristics of telecommunications networks of alarm data. Finally, pose the application of the data mining technology in the telecommunication network alarm correlation analysis. Detailed introduction algorithm for mining association rules and sequential pattern mining algorithm.Association rule mining algorithm is a commonly used method, Apriori algorithm is one of the most classic in the field of association rule mining algorithm, is also a most influential mining Boolean association rules algorithm for frequent itemsets. The algorithm core idea of the algorithm is based on the frequency of the recursive method of set theory,used layer by layer search iterative method. Apriori algorithm engender a large number of Candidate Sets, and repeatedly scan the database. Against to the problem of Apriori algorithm, Jiawei Han, who in 2000 proposed the algorithm for another classic - FP-growth algorithm. The algorithm is based on the FP-tree (Frequent Pattern Tree) to take sub-rule strategy. In mining frequent itemsets of all does not have a candidate itemsets, In this paper, two algorithms were compared, and use Wuhan Telecommunication Alarm Data Management Center for the experimental data, the analysis of experimental results.Sequential pattern mining is an extension of association rule mining, in an orderly sequence of events is composed of many data sets.Squence is a data collection by many orderly events,is an important branch of Data Mining. Used to extract one-dimensional space in an orderly collection of frequent subsets. If the network alarm information base is a time-ordered collection, then the sequential pattern mining can be used to find frequent sequential patterns of alarm, in order to export association rules of alarm. In this paper, based on FP-tree warning Frequent Sequential Pattern Mining Algorithm FSPMFP (Frequent Sequential Pattern Mining based on Frequent Tree) The basic idea is: First of all, through the improvement of FP-tree, data compression will be alert to the frequent pattern tree, and then for the frequent pattern Bottom-up tree to find frequent itemsets of alarm, The final excavation of the timing relationship between alarm.