Efficient algorithms to identify periodic patterns in multiple sequences

Fournier-Viger, Philippe; Li, Zhitian; Lin, Jerry Chun‐Wei; Kiran, R. Uday; Fujita, Hamido

doi:10.1016/j.ins.2019.03.050

Cited by 51 publications

(30 citation statements)

References 14 publications

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“…Furthermore, the Apriori algorithm maintains the downward closure (DC) property to keep the correctness and completeness of discovered ARs. This DC property is also applied to many other tasks of knowledge discovery such as sequential pattern mining (SPM) [11] or weighted pattern mining (WPM) [13]. To improve the mining performance, the frequent-pattern (FP)-tree structure was presented to keep only the frequent 1-itemsets in the tree structure.…”

Section: High Average-utility Itemset Miningmentioning

confidence: 99%

Incrementally updating the high average-utility patterns with pre-large concept

et al. 2020

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High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

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Section: High Average-utility Itemset Miningmentioning

confidence: 99%

Incrementally updating the high average-utility patterns with pre-large concept

et al. 2020

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“…Additionally, the relative support is denoted as support r (P ) = support(P )/|Ω|, similarly to the one already proposed in [28]. As a matter of example, let us consider again the view of the database organized by customers, which was illustrated in Table 3.…”

Section: A Single Level Of Ambiguitymentioning

confidence: 99%

Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records

2020

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The growing demand for eliciting useful knowledge from data calls for techniques that can discover insights (in the form of patterns) that users need. Methodologies for describing intrinsic and relevant properties of data through the extraction of useful patterns, however, work on fixed input data, and the data representation, therefore, constrains the discovered insights. In this regard, this paper aims at providing foundations to make the descriptive knowledge that is extracted by pattern mining more user-centric by relying on flexible data structures defined on two different perspectives: concepts and data records. In this sense, items in data can be grouped into abstract terms through subjective hierarchies of concepts, whereas data records can also be organized based on the users' subjective perspective. A series of easy-to-follow toy examples are considered for each of the two perspectives to demonstrate the usefulness and necessity of the proposed foundations in pattern mining. Finally, aiming at experimentally testing whether classical pattern mining algorithms can be adapted to such flexible data structures, the experimental analysis comprises different methodologies, including exhaustive search, random search, and evolutionary approaches. All these approaches are based on well-known and widely recognized techniques to demonstrate the usefulness of the provided foundations for future research works and more efficient and specifically designed algorithms. Obtained insights demonstrate the importance of working with subjectivity: an item is a type of soda but belongs to a pack, including two or more soda types.

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“…In the domain of data mining from a sequence database, exploiting sequential patterns is an essential task that has been extensively examined [1,3,4,8,9,10,11,14,17,23,27,35]. AprioriAll [1] was the first algorithm designed to solve the sequential-pattern mining problem.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Mining Top-K Closed Sequential Patterns

et al. 2020

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The problem of exploiting Closed Sequential Patterns (CSPs) is an essential task in data mining, with many different applications. It is used to resolve the situations of huge databases or low minimum support (minsup) thresholds in mining sequential patterns. However, it is challenging and needs a lot of time to customize the minsup values for generating appropriate numbers of CSPs desired by users. To conquer this issue, the TSP algorithm for mining top-k CSPs was previously proposed, with k being a given parameter. The algorithm would return the k CSPs which have the highest support values in a database. However, its execution time and memory usage were high. In this paper, an algorithm named TKCS (Top-K Closed Sequences) is proposed to mine the top-k CSPs efficiently. To improve the execution time and memory usage, it uses a vertical bitmap database to represent data. Besides, it adopts some useful strategies in the process of exploiting the top-k CSPs such as: always choosing the sequential patterns with the greatest support values for generating candidate patterns and storing top-k CSPs in an ascending order of the support values to increase the minsup value more quickly. The empirical results show that TKCS has better performance than TSP for discovering the top-k CSPs in terms of both runtime and memory usage. INDEX TERMS closed sequential pattern; data mining; sequential pattern; top-k sequential patterns.

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Efficient algorithms to identify periodic patterns in multiple sequences

Cited by 51 publications

References 14 publications

Incrementally updating the high average-utility patterns with pre-large concept

Incrementally updating the high average-utility patterns with pre-large concept

Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records

An Efficient Method for Mining Top-K Closed Sequential Patterns

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