Design of Parallel Binary Classification Algorithm Based on Hadoop Cluster with MapReduce Framework

• Main Authors: Bing-Da Chin, 秦秉達
• Other Authors: Hung-Ming Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/fu84aw

碩士 === 國立臺中科技大學 === 資訊工程系碩士班 === 103 === With increased amount data today,it is hard to analyze large data on single computer environment efficiently,the hadoop cluster is very important because we can save and large data by hadoop cluster. Data mining plays an important role of data analysis.Because time complexity of the binary-class classification SVM algorithm is a big issue,we design a parallel binary SVM algorithm to slove this problem,and achieve the effect of classifying appropriate data. By leveraging the parallel processing property in MapReduce ,we implement multi-layer binary SVM by MapReduce framework,and run on the hadoop cluster successfully. By designing different parameters of hadoop cluster and using the same data set for training analysis, it shows that the new algorithm can reduce the computation time significantly.