Nowadays, with the rapid development of the Internet, the era of big data has
come. The Internet generates huge amounts of data every day. However,
extracting meaningful information from massive data is like looking for a
needle in a haystack. Data mining techniques can provide various feasible
methods to solve this problem. At present, many sequential rule mining (SRM)
algorithms are presented to find sequential rules in databases with sequential
characteristics. These rules help people extract a lot of meaningful
information from massive amounts of data. How can we achieve compression of
mined results and reduce data size to save storage space and transmission time?
Until now, there has been little research on the compression of SRM. In this
paper, combined with the Minimum Description Length (MDL) principle and under
the two metrics (support and confidence), we introduce the problem of
compression of SRM and also propose a solution named ComSR for MDL-based
compressing of sequential rules based on the designed sequential rule coding
scheme. To our knowledge, we are the first to use sequential rules to encode an
entire database. A heuristic method is proposed to find a set of compact and
meaningful sequential rules as much as possible. ComSR has two trade-off
algorithms, ComSR_non and ComSR_ful, based on whether the database can be
completely compressed. Experiments done on a real dataset with different
thresholds show that a set of compact and meaningful sequential rules can be
found. This shows that the proposed method works.

本文通过最小描述长度 (MDL) 和两个度量标准来进行连续规则的压缩，提出了一种基于设计的连续规则编码方案的 MDL 压缩解决方案 ComSR。实验结果表明，该方法能够找到一组有效的、紧凑且有意义的连续规则，从而实现数据库的压缩，为降低存储空间和传输时间提供了有效的数据挖掘方法。

基于 MDL 的序列规则压缩

MDL-based Compressing Sequential Rules

In many real-world applications, sequential rule mining (SRM) can provide
prediction and recommendation functions for a variety of services. It is an
important technique of pattern mining to discover all valuable rules that
belong to high-frequency and high-confidence sequential rules. Although several
algorithms of SRM are proposed to solve various practical problems, there are
no studies on target sequential rules. Targeted sequential rule mining aims at
mining the interesting sequential rules that users focus on, thus avoiding the
generation of other invalid and unnecessary rules. This approach can further
improve the efficiency of users in analyzing rules and reduce the consumption
of data resources. In this paper, we provide the relevant definitions of target
sequential rule and formulate the problem of targeted sequential rule mining.
Furthermore, we propose an efficient algorithm, called targeted sequential rule
mining (TaSRM). Several pruning strategies and an optimization are introduced
to improve the efficiency of TaSRM. Finally, a large number of experiments are
conducted on different benchmarks, and we analyze the results in terms of their
running time, memory consumption, and scalability, as well as query cases with
different query rules. It is shown that the novel algorithm TaSRM and its
variants can achieve better experimental performance compared to the existing
baseline algorithm.

本文提出了一种高效算法（TaSRM）进行目标时序规则挖掘，并结合实验结果分析了它与现有算法在运行时间、内存消耗、可扩展性以及不同查询规则下的表现，证明了其比基线算法更具实验性能。