学术
摘 要:
为改进基于数据库垂直表示的频繁项集挖掘算法的性能，给出了用索引数组方法来改进计算性能的思路。提出了索引数组的概念及其计算方法，并提出了一种新的高效的频繁项集挖掘算法Index—FIMiner。该算法大大减少了不必要的tidset求交及相应的频繁性判断操作，同时也论证了代表项可直接与其包含索引中的所有项集的组合进行连接，这些结果项集的支持度均与代表项的支持度相等，从而降低了这些频繁项集的处理代价，提高了算法的性能。实验结果表明，Index—FIMiner算法具有较高的挖掘效率。[著者文摘]
文章出处:
《高技术通讯》-2008年18卷3期 -259-264页
栏目信息:
分 类 号:
An efficient frequent itemset mining algorithm based on index array
Song Wei, Yang Bingru , Xu Zhangyan ,Han Yanling（ School of Information Engineering, University of Science and Technology Beijing, Beijing 100083） （ Information College, Shanghai Fisheries University, Shanghai 200090）
Abstract:
To improve the performance of frequent itemset mining algorithms based on vertical representation of database, the idea of using index array to enhance the computing performance is presented. The concept of index array and the corresponding computing method are proposed. Then, a new efficient frequent itemset mining algorithm Index-FIMiner is presented. The algorithm avoids the redundant operations on intersections of tidsets and the corresponding frequency-checking greatly. Meanwhile, it is proved that the representative item can connect all the combinations of items in its subsume index directly, and all the resulting itemsets share the same supports as the representative item. Thus, the cost for processing this kind of itemsets is lowered, and the algorithm efficiency is improved. Experimental results show that IndexFIMiner is efficient.[著者文摘]
Key words:
data mining, association rule, frequent itemset, index array, subsume index
基金资助:
国家自然科学基金（60675030,60463003）和中国博士后科学基金（20060390399）资助项目.
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