相似数据项集方法：一种编码时态数据库技术
Similarity Data Item Set Approach: An Encoded Temporal Data Base Technique
摘要：
数据采掘已被广泛认为是一个强大的工具，探索从大型数据库的附加值。在关联规则的数据采掘过程中，发现数据库中的频繁项集是一个关键问题。为了寻找频繁项集，人们提出了许多算法。本文提出了一个总结和比较研究的可用FP增长算法的变化产生的挖掘频繁项目集显示其能力和效率方面的时间和内存消耗的关联规则挖掘，考虑到具体的信息的应用。提出了基于模式增长挖掘范式的FP-树增长算法，该算法采用树结构来压缩数据库。性能研究表明，反FP-增长方法对于挖掘长、短频繁模式都是有效的、可扩展的，比Apriority算法快一个数量级，也比最近报道的一些新的频繁模式挖掘算法快。
英文摘要：
Data mining has been widely recognized as a powerful tool to explore added value from large-scale databases. Finding frequent item sets in databases is a crucial in data mining process of extracting association rules. Many algorithms were developed to find the frequent item sets. This paper presents a summary and a comparative study of the available FP-growth algorithm variations produced for mining frequent item sets showing their capabilities and efficiency in terms of time and memory consumption on association rule mining by taking application of specific information into account. It proposes pattern growth mining paradigm based FP-tree growth algorithm, which employs a tree structure to compress the database. The performance study shows that the anti- FP-growth method is efficient and scalable for mining both long and short frequent patterns and is about an order of magnitude faster than the Apriority algorithm and also faster than some recently reported new frequent-pattern mining.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
