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With the development of data mining technology and the maturity of mining algorithms, people can find the knowledge they really need from a large number of complicated information (Bagui and Dhar, 2019). Data mining can extract and mine knowledge from a large amount of data, among which web data mining technology is the most applied in the field of data mining.The so-called Web data mining refers to the ability to extract the model that users are interested in from the log data of the network server (Rathee et al. 2015). Essentially, it is to analyze the relationship between log data and applicable resources in the network server. In Web data mining, the Web server contains a large number of Web page information, the user's use of Web page information and the link information between these pages can be used as the object of data mining (Li-Rui and Zhi-Fu, 2015). Traditional data in the database and Web data there are great differences in the structure (Kumar et al., 2020). For example, in the traditional database data structural is very strong, and the data on the Web is a semi-structured, and so on the data on the Web data mining when compared to the traditional database for data mining is more difficult, so the Web log files before excavation, need for these semi-structured data for processing and finishing, and then according to the traditional mining algorithm on the mining processing (Singh et al., 2016).
The mining of Web log files has a strong practical significance, mainly reflected in the following three aspects to provide personalized websites. According to the purpose of Web log mining, appropriate data mining algorithms are selected to obtain the model of the website visited by users, and these models are analyzed to obtain which kind of websites the users like, so that the next time the users log in the website again, they can provide specific suggestion pages for browsing.In order to facilitate users to access these Web pages quickly next time, these Web pages can be assigned to different servers to solve the problem of network traffic congestion. Web relevance analysis for analyzing which pages are closely related to determine whether to add links to increase the speed of web page access (Mane and Ghorpade, 2016). For example, if you have a lot of people have such access patterns: (1), (2), (3), (4) (1), (2), (3), (4) represent different web page, can then be determined between (1) and (3) or (2) and (4) there is a certain relationship, can consider to whether links added directly on (1) (3), add (4) in the (2) directly links, to accelerate the speed of the user to access the target page.
In web log mining, questions are raised about how to solve the above three problems.Meanwhile, researchers found that the application of association rule algorithm in data mining to Web log mining can solve these three problems well.Based on the requirements of actual operation and maintenance scenarios, this paper improves the Apriori algorithm and applies it to the sequence information scenario of operation and maintenance alarm. The results show that significant improvement is achieved in the result and efficiency of alarm combination.