基于混合单调分解的文件方言无监督聚类
Unsupervised clustering of file dialects according to monotonic decompositions of mixtures
AI总结：
本文提出一种基于消息共现模式的无监督文件方言聚类方法，通过贪心算法从文件-消息矩阵中推导候选方言，并证明其最优性条件，从而降低分析师研究复杂格式的认知负担。
摘要：
本文提出一种无监督分类方法，根据文件的行为将一组文件划分为互不重叠的方言，这些行为由消费这些文件的一组程序所产生的消息决定。消息模式可以作为特定行为类型的签名，同时理解到某些消息可能共同出现，而其他消息则不会。消息模式可用于将文件分类为方言。方言由消息的一个子集定义，称为必需消息。一旦文件以方言及其必需消息为条件，其余消息在统计上相互独立。基于这一定义，我们提出一种贪心算法，从由文件-消息数据矩阵组成的数据集中推导候选方言，在多种文件格式上展示其性能，并证明其在何种条件下是最优的。我们表明，分析师需要考虑的方言数量少于不同的消息模式数量，这降低了他们在研究复杂格式时的认知负担。
英文摘要：
This paper proposes an unsupervised classification method that partitions a set of files into non-overlapping dialects based upon their behaviors, determined by messages produced by a collection of programs that consume them. The pattern of messages can be used as the signature of a particular kind of behavior, with the understanding that some messages are likely to co-occur, while others are not. Patterns of messages can be used to classify files into dialects. A dialect is defined by a subset of messages, called the required messages. Once files are conditioned upon dialect and its required messages, the remaining messages are statistically independent. With this definition of dialect in hand, we present a greedy algorithm that deduces candidate dialects from a dataset consisting of a matrix of file-message data, demonstrate its performance on several file formats, and prove conditions under which it is optimal. We show that an analyst needs to consider fewer dialects than distinct message patterns, which reduces their cognitive load when studying a complex format.
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。
