Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion

Lin, Jerry Chun‐Wei; Zhang, Yuyu; Fournier-Viger, Philippe; Hong, Tzung-Pei

doi:10.1007/s40815-018-0520-5

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…scan fuzzy semantic trajectory database FD (4) calculate the support P of each sequence (5) calculate the fuzzy stay time membership S(Δt) of each sequence accord to the time membership function (6) if P < σ and μ A j (Δt) < ρ (7) the sequence is less than the minimum support threshold and fuzzy stay time membership threshold (8) define the sequence as an infrequent itemset and delete it (9) create a new sequence database Q i and add frequent itemsets with length i to the database (10) for each frequent itemset T in the sequence database Q i (11) construct the projection database C w of frequent itemset T (12) get the frequent itemset T k of C w (13) T and T k are constructed as frequent sequences T i with length i (14) FTFP � FTFP ∪ T i (15) if projection database C w is not empty (16) repeat steps 8-12, i � i + 1 (17) else output frequent sequence FTFP (18) end for (19) end if (20) return FTFP ALGORITHM 2: FST-FPM algorithm.…”

Section: Fst-fpm Algorithm Descriptionmentioning

confidence: 99%

“…Finally, fuzzy association rules are derived by using these fuzzy values. Lin et al [15] proposed an incremental multiple fuzzy frequent item mining algorithm based on transaction insertion (IMF-INS) to effectively update multiple fuzzy frequent itemsets of quantitative datasets. IMF-INS uses fuzzy FUP concept to divide the converted language terms into four cases.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

Gao

et al. 2022

Scientific Programming

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In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.

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Section: Fst-fpm Algorithm Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

Gao

et al. 2022

Scientific Programming

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“…Researchers have focused on the fuzzification of any one of the parameters such as time [24,26,27], or quantity [28,29,30]. HUFI-Miner algorithm performed quantity fuzzification in the domain of utility mining [28].…”

Section: Introductionmentioning

confidence: 99%

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

Ritika

Gupta²

2022

IEEE Access

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The field of data mining is progressing rapidly presenting researchers with many opportunities for research. Sequential pattern mining is a popular and long established technique in data mining which extracts data in the form of sequential patterns satisfying a threshold value such as utility, support, profit or a combination of these. The application of fuzzy theory in sequential pattern mining has also been favored leading to more natural liinguistic representation. Researchers have proposed various hybrid algorithms with fuzzification of any one parameter such as time or quantity. This paper proposes a hybrid fuzzy algorithm for mining of frequent and high utility sequential patterns with fuzzification of both purchase time and purchase quantity parameters, thereby giving more useful fuzzy sequential patterns. Experimental results also prove that the proposed algorithm is better than the existing algorithms. INDEX TERMS Fuzzy theory, high utility mining, maximum measure, sequential pattern mining, timeintervals VOLUME 4, 2016

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“…Anuradha et al [21] used type-2 fuzzy sets to mine multi-level association rules. Lin et al [22] used type-2 fuzzy sets to discover fuzzy frequent item sets efficiently. Kalia et al [23] used type-2 fuzzy sets and genetic algorithms to mine fuzzy rules in highdimensional classification tasks.…”

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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Efficiently Updating the Discovered Multiple Fuzzy Frequent Itemsets with Transaction Insertion

Cited by 13 publications

References 31 publications

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

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

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