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1. PrefixFPM: A Parallel Framework for General-Purpose Frequent Pattern Mining

   Yan, Da; Qu, Wenwen; Guo, Guimu; Wang, Xiaoling (January 2020, Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE))

   Frequent pattern mining (FPM) has been a focused theme in data mining research for decades, but there lacks a general programming framework that can be easily customized to mine different kinds of frequent patterns, and existing solutions to FPM over big transaction databases are IO-bound rendering CPU cores underutilized even though FPM is NP-hard. This paper presents, PrefixFPM, a general-purpose framework for FPM that is able to fully utilize the CPU cores in a multicore machine. PrefixFPM follows the idea of prefix projection to partition the workloads of PFM into independent tasks by divide and conquer. PrefixFPM exposes a unified programming interface to users who can customize it to mine their desired patterns, and the parallel execution engine is transparent to end-users and can be reused for mining all kinds of patterns. We have adapted the state-of-the-art serial algorithms for mining frequent patterns including subsequences, subtrees, and subgraphs on top of PrefixFPM, and extensive experiments demonstrate an excellent speedup ratio of PrefixFPM with the number of cores. A demo is available at https://youtu.be/PfioC0GDpsw; the code is available at https://github.com/yanlab19870714/PrefixFPM. 

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2. Parallel Mining of Frequent Subtree Patterns

   https://doi.org/10.1007/978-3-030-61133-0_2  

   Qu, Wenwen; Yan, Da; Guo, Guimu; Wang, Xiaoling; Zou, Lei; Zhou, Yang (November 2020, Communications in computer and information science)

   null (Ed.)

   Mining frequent subtree patterns in a tree database (or, forest) is useful in domains such as bioinformatics and mining semi-structured data. We consider the problem of mining embedded subtrees in a database of rooted, labeled, and ordered trees. We compare two existing serial mining algorithms, PrefixTreeSpan and TreeMiner, and adapt them for parallel execution using PrefixFPM, our general-purpose framework for frequent pattern mining that is designed to effectively utilize the CPU cores in a multicore machine. Our experiments show that TreeMiner is faster than its successor PrefixTreeSpan when a limited number of CPU cores are used, as the total mining workloads is smaller; however, PrefixTreeSpan has a much higher speedup ratio and can beat TreeMiner when given enough CPU cores. 

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3. Mining Order-preserving Submatrices under Data Uncertainty: A Possible-world Approach and Efficient Approximation Methods

   https://doi.org/10.1145/3524915  

   Cheng, Ji; Yan, Da; Qu, Wenwen; Hao, Xiaotian; Long, Cheng; Ng, Wilfred; Wang, Xiaoling (January 2022, ACM transactions on database systems)

   Given a data matrix 𝐷, a submatrix 𝑆 of 𝐷 is an order-preserving submatrix (OPSM) if there is a permutation of the columns of 𝑆, under which the entry values of each row in 𝑆 are strictly increasing. OPSM mining is widely used in real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise is ubiquitous in real data matrices due to variable experimental conditions and measurement errors, which makes conventional OPSM mining algorithms inapplicable. No previous work on OPSM has ever considered uncertain value intervals using the well-established possible world semantics. We establish two different definitions of significant OPSMs based on the possible world semantics: (1) expected support-based and (2) probabilistic frequentness-based. An optimized dynamic programming approach is proposed to compute the probability that a row supports a particular column permutation, with a closed-form formula derived to efficiently handle the special case of uniform value distribution and an accurate cubic spline approximation approach that works well with any uncertain value distributions. To efficiently check the probabilistic frequentness, several effective pruning rules are designed to efficiently prune insignificant OPSMs; two approximation techniques based on the Poisson and Gaussian distributions, respectively, are proposed for further speedup. These techniques are integrated into our two OPSM mining algorithms, based on prefix-projection and Apriori, respectively. We further parallelize our prefix-projection-based mining algorithm using PrefixFPM, a recently proposed framework for parallel frequent pattern mining, and we achieve a good speedup with the number of CPU cores. Extensive experiments on real microarray data demonstrate that the OPSMs found by our algorithms have a much higher quality than those found by existing approaches. 

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4. Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey

   https://doi.org/10.1109/TBDATA.2021.3072001  

   Jazayeri, Ali; Yang, Chris (December 2022, IEEE Transactions on Big Data)

   Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks representing multiple independent systems or one system at multiple time stamps. The cores of mining frequent subgraphs are graph and subgraph isomorphism. Due to the complexities of these problems, the frequent subgraph mining algorithms proposed in the literature employ various heuristics for candidate generation, duplicate subgraphs pruning, and support computation. In this survey, we provide a classification of proposed algorithms in the literature. The algorithms for static networks have found numerous applications. Therefore, these algorithms will be reviewed in detail. Besides, it is discussed that consideration of temporality of data can impact the derived insight and attracted substantial attention in recent years. However, prior surveys have not comprehensively examined the algorithms of frequent subgraph mining in a database of temporal networks represented as network snapshots. Therefore, the algorithms proposed for mining frequent subgraphs in temporal networks are reviewed. Moreover, most of the surveys have focused on main-memory algorithms. Here, we review disk-based, parallel, and distributed algorithms proposed for mining frequent subgraphs. 

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5. NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining

   https://doi.org/10.1145/3480245  

   Wu, Youxi; Luo, Lanfang; Li, Yan; Guo, Lei; Fournier-Viger, Philippe; Zhu, Xingquan; Wu, Xindong (June 2022, ACM Transactions on Knowledge Discovery from Data)

   Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according to three levels of interest: strong, medium, and weak interest. NTP mining can effectively avoid mining redundant patterns since the NTPs are composed of strong and medium interest items. Moreover, NTPs can avoid serious deviations (the occurrence is significantly different from its pattern) since gap constraints cannot match with strong interest patterns. To mine NTPs, an effective algorithm is put forward, called NTP-Miner, which applies two main steps: support (frequency occurrence) calculation and candidate pattern generation. To calculate the support of an NTP, depth-first and backtracking strategies are adopted, which do not require creating a whole Nettree structure, meaning that many redundant nodes and parent–child relationships do not need to be created. Hence, time and space efficiency is improved. To generate candidate patterns while reducing their number, NTP-Miner employs a pattern join strategy and only mines patterns of strong and medium interest. Experimental results on stock market and protein datasets show that NTP-Miner not only is more efficient than other competitive approaches but can also help users find more valuable patterns. More importantly, NTP mining has achieved better performance than other competitive methods in clustering tasks. Algorithms and data are available at: https://github.com/wuc567/Pattern-Mining/tree/master/NTP-Miner . 

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