Improving the techniques of Mining Association Rule based on MapReduce framework

• Main Authors: Bo-Ting Chen, 陳柏廷
• Other Authors: Shih-Ying Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/b6g4eb

碩士 === 國立臺中科技大學 === 資訊工程系碩士班 === 103 === We can get useful and valuable information from insignificant data through data mining and gain huge benefit from professional analysis. However, it is important to improve the performance of data mining for Big Data processing. The purpose of this study is to improve the performance of parallel association-rule mining algorithm of PIETM (Principle of Inclusion- Exclusion and Transaction Mapping) under the MapReduce framework. PIETM is arranging transaction data in database into a tree structure which is called Transaction tree (T-tree), and then transform T-tree into Transaction Interval tree (TI-tree). And use principle of Inclusion- Exclusion according to TI-tree to calculate all frequent itemsets. PIETM combines the benefits of Apriori and FP-growth algorithms and only needs to scan the database twice in data mining. However, we still need to improve some procedures, for example, construct a transaction tree and generate candidate k-item sets. For the two problems described above, we provide a solution respectively. These two solutions adopted the FP-growth and Apriori algorithms respectively.