挖掘闭合模式的高性能算法

频繁闭合模式集惟一确定频繁模式完全集并且尺寸小得多,然而挖掘频繁闭合模式仍然是时间与存储开销很大的任务.提出一种高性能算法来解决这一难题.采用复合型频繁模式树来组织频繁模式集,存储开销较小.通过集成深度与宽度优先策略,伺机选择基于数组或基于树的模式支持子集表示形式,启发式运用非过滤虚拟投影或过滤型投影,实现复合型频繁模式树的快速生成.局部和全局剪裁方法有效地缩小了搜索空间.通过树生成与剪裁代价的平衡实现时间效率与可伸缩性最大化.实验表明,该算法时间效率比其他算法高5倍到3个数量级,空间可伸缩性最佳.它可以进一步应用到无冗余关联规则发现、序列分析等许多数据挖掘问题.

Mining Frequent Closed Patterns by Adaptive Pruning

The set of frequent closed patterns determines exactly the complete set of all frequent patterns and is usually much smaller than the laster. Yet mining frequent closed patterns remains to be a memory and time consuming task. This paper tries to develop an efficient algorithm to solve this problem. The compound frequent item set tree is employed to organize the set of frequent patterns, which consumes much less memory than other structures. The tree is grown quickly by integrating depth first and breadth first search strategies, opportunistically choosing between two different structures to represent projected transaction subsets, and heuristically deciding to build unfiltered pseudo or filtered projections. Efficient pruning methods are used to reduce the search space. The balance of the efficiency and scalability of tree growth and pruning maximizes the performance. The experimental results show that the algorithm is a factor of five to three orders of magnitude more time efficient than several recently proposed algorithms, and is also the most scalable one. It can be used in the discovery of non-redundant association rules, sequence analysis, and many other data mining problems.