An Efficient Data Mining Approach on Compressed Transactions

• Main Authors: Jia-Yu Dai, 戴家裕
• Other Authors: Don-Lin Yang
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/89346185034760852262

碩士 === 逢甲大學 === 資訊工程所 === 95 === In an era of knowledge explosion, the growth of data is rapidly increasing day by day. However, there is always not enough storage to store these data. How to reduce the data space becomes a challenge issue. Data compression provides a good solution which can lower the required space. Data mining has many useful applications in recent years, because it can help users discover new knowledge in large databases. However, existing compression algorithms are not appropriate for data mining. In [1, 2], two different approaches were proposed to compress databases and then perform data mining process. However, they all lack the ability to decompress the data to their original state and the improvements for data mining performance. In this research we propose a new approach, Merging Relevant Transactions Approach, to solve these problems. This approach uses the relationship of transactions to merge related transactions and builds a quantification table to prune candidate itemsets which are impossible to become frequent in order to improve the performance of mining association rules.