A Novel Selection and Matching of Patterns Model for Traffic Prediction in Wireless Network

Abstract

Data mining techniques are becoming increasing popular and important in the big data era. Among them, frequent patterns mining technique has matured, with this technique, we can obtain the association rules to predict the data in cellular network. Existing research mainly focus on the frequent item sets mining, rarely involves in the time series and FPM algorithm usually used user-defined minimum support in various training datasets, which is rigorous, may influence the number of the frequent patterns and fail to select the reasonable patterns. Moreover, traditional association rules mining only relies on single evaluation criterion such as confidence or support, which leads to select unsound rules. This paper introduces an optimized frequent pattern mining algorithm, improving the setting of support in the process of mining frequent sequences and presenting a new evaluation criterion for the candidate patterns. Experiments were conducted to select appropriate parameters, support and evaluation criterion. Furthermore, we apply the above conclusion to the cellular flow data prediction, and compare the runtime, matching rate, RMSE and MAPE of proposed algorithms with those of improved Markov algorithm to examine the effectiveness of algorithm.