Outlier detection is a very important type of data mining,which is extensively used in application areas.The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data,but also uses lots of machine resources,which results in the imbalance of the machine load.This paper presents an algorithm of the MapReduce-based and cell-based outlier detection,combined with the single-layer perceptron,which achieves the parallelization of outlier detection.These experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.
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机译:LTE通讯系统中针对同层干扰环境对微小型基地台功率控制与用户位置推荐演算法 =Femtocell Power Control and User Location Recommendation Algorithm for Co-Tier Interference Environment in LTE Communication System