Using of Rough Set Theory to Increase the Performance of Big Data with Uncertainty

• Main Authors: CHEN, YEN-AN, 陳彥安
• Other Authors: HUANG, KUANG-YU
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/94520591221420675302

碩士 === 嶺東科技大學 === 資訊管理系碩士班 === 104 === Since the advent of Big Data era, large amounts of data are constantly being produced and stored; and research institutions International Data Corporation also pointed out that the global amount of data is growing at an annual rate of 50% growth forecast the next six years, the amount of data will grow 10 times as much, but because the government appears after the word big data, all data saved none of its filtration, thus resulting in relatively lower availability of information, how to improve the availability of information has become the most attention by one of the topics; secondly, when the use of the non-parallel processing of large data type data, face restrictions processing speed, memory and storage space. Therefore, this study presents the data mining technology and distributed parallel computing technology to be integrated, the large-volume data and simplify the analysis. Research using data mining the Data Reduction (DR) technology, to be considered for Record Reduction and Value Reduction. Data mining technology system using Fuzzy C-mean and Rough Set Theory reached streamline data purposes. First (1) through the FCM will information be clustering; and then (2) the use of RST derive more condensed knowledge rules; and (3) through an approximate collection concept RST's, from the original data set, the data set is not accurate data sets to be singled out. In addition to using the above two techniques to achieve DR goals, thus making the follow-up information becomes more clear and effective, significantly increasing the availability of information, thus making the analysis and application of large amounts of data on the entire parallel computing platform used Hadoop Cloud .