基于FP-growth的关联规则算法的实现与应用

中 文 题 目：基于 FP-growth 的关联规则算法的实现与应用I摘 要伴随着计算机与信息技术的发展，迎来了云计算时代。利用手机、电脑等电子数码产品来记录人们的生活，存储数据，数据呈指数增长。大数据分析技术也吸引了越来越多的关注。大数据并不在于“大”，而在于有用，其价值含量比数量更重要。大数据与数据挖掘对于发展进步的中国和各行各业许多领域有着举足轻重的影响力，是学者们努力研究与应用的方向之一。在关联规则挖掘领域最经典的算法是 Apriori，其致命的缺点是需要多次扫描事务数据库。于是人们提出了各种裁剪数据集的方法以减少 I/O 开支，本文所重点研究的 FP-growth 算法就是其中最高效的一种。本文介绍了关联规则的概念与经典算法——Apriori 算法与 FP-growth 算法，以及两种基于 FP-growth算法提出的适合于挖掘大型数据库的关联规则算法。运用实例分析事务数据库中不同项之间的关系，进而分析投票者的投票，以便得知投票者的党派偏向。关键词：数据挖掘；关联规则；FP-growth 算法III山西财经大学 2020 届本科生毕业论文（设计）AbstractWith the development of computer and information technology, ushered in the era of cloud computing.people use mobile phones, computers and other electronic digital products to record people’s lives, store data, the data grows exponentially. With the advent of the cloud era, big data analytics technology has attracted more and more attention. Big data does not focus on “big”, but on useful, and its value content is more important than quantity. Big data and data mining have a significant influence on the development and progress of China and every trade and many domains, and it is one of the directions that scholars are trying to study and apply. The most classical algorithm in the field of association rule mining is Apriori, whose fatal disadvantage is that it needs to scan the transaction database multiple times. Therefore, various methods of clipping data sets have been proposed to reduce I/O expenses. The FP-growth algo...