Linguistic frequent pattern mining using a compressed structure

Lin, Jerry Chun‐Wei; Ahmed, Usman; Srivastava, Gautam; Wu, Jimmy Ming‐Tai; Hong, Tzung-Pei; Djenouri, Youcef

doi:10.1007/s10489-020-02080-w

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…Sequential pattern mining has been widely applied in many fields [ 43 , 44 ], such as ICU patient risk prediction [ 45 , 46 ] and public reactioms analysis on twitter [ 47 ]. A variety of mining methods have been investigated [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

NetNMSP: Nonoverlapping maximal sequential pattern mining

Zhang

Guo

et al. 2022

Appl Intell

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Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

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Section: Related Workmentioning

confidence: 99%

NetNMSP: Nonoverlapping maximal sequential pattern mining

Zhang

Guo

et al. 2022

Appl Intell

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“…Threads were collaborated to generate frequent itemsets in a big data environment. A compressed fuzzy list structure was designed to extract frequent itemsets [5]. This helped in avoiding repeated screening of the dataset.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Poovan

Acharya

Reddy

2023

IJECE

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Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.

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“…Molina et al [26] used a hierarchy to represent fuzzy association rules. Lin et al [27] quickly discovered fuzzy frequent item sets from quantitative database based on type-2 fuzzy sets. Zhang et al [28] mined the relationship between different types of crime rate based on fuzzy association rules.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets

Guo

Wang

2022

Applied Sciences

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The goal of spatial co-location pattern mining is to find subsets of spatial features whose instances are often neighbors in a geographical space. In many practical cases, instances of spatial features contain not only spatial location information but also attribute information. Although there have been several studies that use type-1 fuzzy membership functions to mine spatial fuzzy co-location patterns, there is great uncertainty associated with such membership functions. To address this problem, we propose a spatial fuzzy co-location pattern mining method based on interval type-2 fuzzy sets. First, we collect the interval evaluation values of the interval data of attribute information from experts to form granular data. Next, the original type-1 fuzzy membership function is extended to a granular type-2 fuzzy membership function based on elliptic curves. We use a gradual method to adjust the parameters of the fuzzy membership function so that its footprint of uncertainty satisfies both the connectivity and the given confidence. Based on this granular type-2 fuzzy membership function, we fuzzify the attribute information of instances and define the concepts of fuzzy features and fuzzy co-location patterns. A fuzzy co-location pattern mining algorithm based on spatial cliques is then proposed, termed the FCPM-Clique algorithm. In order to improve the efficiency of the algorithm, we propose two pruning strategies. In addition, we extend two classical spatial pattern mining algorithms, the Join-based algorithm and the Joinless algorithm, to mine fuzzy co-location patterns based on interval type-2 fuzzy sets. Many experiments on synthetic and real-world datasets are conducted, the performance of the three algorithms is compared, and the effectiveness and efficiency of our proposed FCPM-Clique algorithm is demonstrated.

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Linguistic frequent pattern mining using a compressed structure

Cited by 8 publications

References 45 publications

NetNMSP: Nonoverlapping maximal sequential pattern mining

NetNMSP: Nonoverlapping maximal sequential pattern mining

A genetic algorithm coupled with tree-based pruning for mining closed association rules

A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets

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