Discovering Periodic Patterns in Non-uniform Temporal Databases

Kiran, R. Uday; Venkatesh, Janani; Fournier-Viger, Philippe; Toyoda, Masashi; Reddy, P. Krishna

doi:10.1007/978-3-319-57529-2_47

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…Episode mining can be used to analyze various types of data such as web-click streams, telecommunication data, sensor readings, sequences of events on an assembly line, and network traffic data. 104,105 • Periodic pattern mining [106][107][108] is the problem of discovering patterns in a single sequence of transactions. The goal of periodic pattern is not just to find patterns that regularly appear in a sequence, but that also appear periodically.…”

Section: Other Pattern Mining Problems Related To Itemset Miningmentioning

confidence: 99%

A survey of itemset mining

Fournier‐Viger

Lin

et al. 2017

WIREs Data Min & Knowl

194

137

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Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules

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Section: Other Pattern Mining Problems Related To Itemset Miningmentioning

confidence: 99%

A survey of itemset mining

Fournier‐Viger

Lin

et al. 2017

WIREs Data Min & Knowl

194

137

View full text Add to dashboard Cite

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“…This is especially important with the emergence of Big data. Over nearly 30 past years, various itemset have been identified, namely sequential and time-series itemset [32], [33], [34], high utility itemset [35], [36], [37], structural itemset [38], [39], temporal (periodic) itemset [40], [41], [42], [43].…”

Section: A Frequent Itemset Miningmentioning

confidence: 99%

“…However, all these algorithms have a drawback that, if only one of the periods of a itemset exceeds maxPer, this itemset is discarded. Kiran et al [40] proposed a model called partial PFP mining that relaxes the maximum periodicity constraint by considering that a itemset X is (partial) periodic if its periodic-frequency is no less than a user-specified threshold. However, this algorithm cannot be applied to find stableperiodic-frequent itemset.…”

Section: B Periodic-frequent Itemset Miningmentioning

confidence: 99%

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

et al. 2023

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Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.INDEX TERMS Columnar databases, stable periodic-frequent itemset, itemset mining. I. INTRODUCTIONDatabase systems play a crucial role in storing the big data generated by real-world applications. Depending on the layout used for storing the data, one can broadly classify the databases into two types: row databases and columnar databases. 1 Row databases are primarily based on ACID 2 properties and organize the data as records by keeping the data associated with a record next to each other in a storage device. The popular row databases include MySQL [1] and Postgres [2]. In contrast, columnar databases are based on BASE 3 properties and organize data into fields and storeThe associate editor coordinating the review of this manuscript and approving it for publication was Laura Celentano .1 Columnar and row databases are referred as vertical and horizontal databases, respectively.2 ACID is an acronym for Atomicity, Consistency, Isolation, and Duration.3 BASE is an acronym for Basically Available, Soft state, and Eventually consistent.

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“…Some researchers have developed algorithms to mine periodic frequent patterns (PFPs) in transaction databases in the area of frequent pattern mining (FPM) [20,[30][31][32][33]. Most of these algorithms relied on the excellent tree-based data structures to produce an entire collection of periodic-frequent patterns in a transactional database.…”

Section: Periodic High-utility Sequential Pattern Miningmentioning

confidence: 99%

“…These problems also exist in periodic frequency pattern mining (PFPM) [17][18][19]. In order to provide greater flexibility, Kiran et al proposed the partial periodic frequency pattern mining (PPFPM) algorithm [20]. This algorithm relaxes the maxPer threshold constraint, allowing a specific amount of periods beyond it.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Mining Stable Periodic High-Utility Sequential Patterns

Xie

Zhao

2022

Symmetry

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Periodic high-utility sequential pattern mining (PHUSPM) is used to extract periodically occurring high-utility sequential patterns (HUSPs) from a quantitative sequence database according to a user-specified minimum utility threshold (minutil). A sequential pattern’s periodicity is determined by measuring when the frequency of its periods (the time between two consecutive happenings of the sequential pattern) exceed a user-specified maximum periodicity threshold (maxPer). However, due to the strict judgment threshold, the traditional PHUSPM method has the problem that some useful sequential patterns are discarded and the periodic values of some sequential patterns fluctuate greatly (i.e., are unstable). In frequent itemset mining (FIM), some researchers put forward some strategies to solve these problems. Because of the symmetry of frequent itemset pattern (FIPs), these strategies cannot be directly applied to PHUSPM. In order to address these issues, this work proposes the stable periodic high-utility sequential pattern mining (SPHUSPM) algorithm. The contributions made by this paper are as follows. First, we introduce the concept of stability to overcome the abovementioned problems, mine sequential patterns with stable periodic behavior, and propose the concept of stable periodic high-utility sequential patterns (SPHUSPs) for the first time. Secondly, we design a new data structure named the PUL-list to record the periodic information of sequential patterns, thereby improving the mining efficiency. Thirdly, we propose the maximum lability pruning strategy in sequential pattern (MLPS), which can prune a large number of unstable sequential patterns in advance. To assess the algorithm’s effectiveness, we perform many experiments. It turns out that the algorithm can not only mine patterns that are ignored by traditional algorithms, but also ensure that the discovered patterns have stable periodic behavior. In addition, after using the MLPS pruning strategy, the algorithm can prune 46.5% of candidates in advance on average in six datasets. Pruning a large number of candidates in advance not only speeds up the mining process, but also greatly reduces memory usage.

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Discovering Periodic Patterns in Non-uniform Temporal Databases

Cited by 22 publications

References 8 publications

A survey of itemset mining

A survey of itemset mining

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

An Efficient Algorithm for Mining Stable Periodic High-Utility Sequential Patterns

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