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An evolutionary algorithm for mining rare association rules: a Big Data approach

机译：采矿稀有关联规则的进化算法：大数据方法

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摘要

Association rule mining is one of the most well-known techniques to discover interesting relations between items in data. To date, this task has been mainly focused on the discovery of frequent relationships. However, it is often interesting to focus on those that do not occur frequently. Rare association rule mining is an alluring field aiming at describing rare cases or unexpected behavior. This field is really useful over Big Data where abnormal endeavor are more curious than common behavior. In this sense, our aim is to propose a new evolutionary algorithm based on grammars to obtain rare association rules on Big Data. The novelty of our work is that it is eminently designed to be parallel, enabling its use over emerging technologies as Spark and Flink. Furthermore, while other algorithms focus on maximizing a couple of quality measure ignoring the rest, our fitness function has been precisely designed to obtain a trade-off while maximizing a set of well-known quality measures. The experimental study includes more than 70 datasets revealing alluring results in efficiency when more than 300 million of instances and file sizes up to 250 GBytes are considered, and proving that it is able to run efficiently in huge volumes of data.
机译：关联规则挖掘是最着名的技术之一，可以发现数据中项目之间有趣关系的技术之一。迄今为止，这项任务主要集中在发现频繁的关系中。但是，专注于那些不会经常发生的人往往有趣。罕见的协会规则挖掘是一个诱人的领域，旨在描述罕见的病例或意外行为。这个领域对大数据真实有用，其中异常努力比共同行为更加好奇。从这个意义上讲，我们的目标是提出基于语法的新进化算法，以获得大数据的罕见关联规则。我们的作品的新颖之处在于它非常旨在平行，使其在新兴技术用作Spark和Flink。此外，虽然其他算法专注于最大化几个忽略其余的质量措施，但我们的健身功能精确地设计以获得折衷，同时最大化一套众所周知的质量措施。实验研究包括超过70个数据集，当考虑超过250个Gbytes的300亿个实例和文件大小时，揭示了效率的效率导致，并证明它能够以大量的数据有效运行。

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