Research And Implementation Based The Incremental Clustering Mobile Phone Virus Mining Technology

With the continuous advancement of information technology. Mobile phones become increasingly indispensable in people’s lives. At the same time, the mobile phone virus along quietly into the people’s lives. Today, the computer virus is ever-changing. The field of mobile phone virus is not standing still. The field of mobile phone viruses appear the way Hybrid infection. When users use the phone to install a new application, there will be a very big security risk. To solve this problem, it is necessary to develop a simple and efficient mining engine on the mobile phone viruses excavation. The topic from a large foreign cooperation projects. The main task is the development and testing of clustering mining module.This paper first describes the basic concepts and common types of mobile phone virus. Analysis of the various types of the virus and attack mechanisms.Describe the dangers of mobile phone virus. And several relatively common virus prevention technology.Introduced to the basics of data mining and commonly used clustering mining algorithms. Delve into clustering mining technology. Illustrate the clustering algorithms typically use the storage structure. Carried out a comparative study of common K-means algorithm and DBSCAN algorithm. As the full theoretical and technical reserves for the next step in the research. This work determines the incremental algorithm based on K-means to deal with mobile phone virus incremental mining problems.This paper summarizes the experience of their predecessors. Taking into account the specific application requirements of the mobile phone virus mining. Improve and enhance the K-means algorithm. Through the application of a normalized data, phone virus mining accuracy increased by15%on average. Designed to achieve incremental algorithm based on K-means algorithm. Can effectively incremental mining the results of K means mining. Corresponding optimization algorithm memory usage, and many other. A50%reduction in memory footprint under the same conditions. Finally, the experimental results are summarized. Summarizes the algorithm suitable for the application scenario. Summarizes the clustering quality influencing factors. Provide a good guidance for future algorithm uses.