A MapReduced-Based and Cell-Based Outlier Detection Algorithm

作     者：ZHU Sunjing LI Jing HUANG Jilin LUO Simin PENG Weiping 

作者机构：School of ComputerWuhan University Faculty of BusinessLahti University of Applied Sciences School of Power and MechanicalWuhan University 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2014年第19卷第3期

页      面：199-205页

学科分类：08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA040910) 

主　　题：outlier MapReduce data mining cell massive data 

摘      要：Outlier detection is a very important type of data mining,which is extensively used in application *** 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 *** 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 *** experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.