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

This paper establishes the theoretical framework of b-bit minwise hashing.
The original minwise hashing method has become a standard technique for
estimating set similarity (e.g., resemblance) with applications in information
retrieval, data management, social networks and computational advertising.
By only storing the lowest $b$ bits of each (minwise) hashed value (e.g., b=1
or 2), one can gain substantial advantages in terms of computational efficiency
and storage space. We prove the basic theoretical results and provide an
unbiased estimator of the resemblance for any b. We demonstrate that, even in
the least favorable scenario, using b=1 may reduce the storage space at least
by a factor of 21.3 (or 10.7) compared to using b=64 (or b=32), if one is
interested in resemblance > 0.5.

本文建立了 b 位最小哈希的理论框架，通过仅存储每个（最小化）哈希值的最低 b 位，可以在计算效率和存储空间方面获得相当大的优势，提供了任何 b 的相似度的无偏估计，即使在最不利的情况下，使用 b = 1 也可以将存储空间至少减少 21.3（或 10.7）倍，如果对相似度 > 0.5 感兴趣。