Design An Efficient Incremental Data Mining Algorithm Based on Pre_large Technique

• Main Authors: Lin shih-chi, 林仕奇
• Other Authors: K.M. Yu
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/87031983635742734639

碩士 === 中華大學 === 資訊管理學系 === 94 === The rapid improvement of information technology and extensive use of computer systems have made the electronic-era realized. The ubiquitous computer systems not only promote the efficiency of our work but also bring about the exponential growth of data volume stored. For the comparatively slow development of computer hardware computing capability, it is an important issue to develop a more efficient incremental data mining algorithm. The thesis focuses on the developing an incremental data mining algorithm of the association rules. We proposed an Pre_large Descending frequent pattern Binary-tree Algorithm(PDBA), PDBA tries to maintain the correctness of the association rules without re-processing the processed data by retaining the last-mined related information. Also, by using the binary tree data structure known for its fast speed of traversing and the Pre_large concept, PDBA cuts down the variation probability of items between high frequency and non-high frequency. PDBA also reduced the number of times needed to scan the database and brought up a more efficient and reliable incremental data mining results of the association rules. For verifying the performance of PDBA, we also implement PDBA along with DFPBT and AFPIM, to compare their efficiency. The experimental results show that PDBA outperforms DFPBT and AFPIM at least 23% in execution time.