Discovery of Interesting Itemsets for Web Service Composition Using Hybrid Genetic Algorithm

Kannimuthu, S.; Chakravarthy, D. Gowtham

doi:10.1007/s11063-022-10793-x

Cited by 8 publications

(4 citation statements)

References 56 publications

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“…To solve one of the main challenges in mining high-utility itemsets from transaction databases which are defning a databasedependent minimum utility threshold, Kannimuthu and Premalatha [40] proposed the stellar mass black hole optimization algorithm to extract top-k high-utility itemsets from the transaction database without defning a minimum utility threshold. Kannimuthu and Chakravarthy [41] presented a hybrid genetic algorithm to extract high-utility itemsets for web service composition and showed acceptable results in terms of processing time and memory usage. A high-utility itemset mining process has an inherent problem of producing a huge number of itemsets as the downward closure property which is applied in frequent itemset mining is not applicable in high-utility mining, and the existing algorithms do not support itemsets associated with negative utility values.…”

Section: Related Workmentioning

confidence: 99%

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

Alrahwan,

Farouk

2024

International Journal of Intelligent Systems

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Data mining is the process used for extracting hidden patterns from large databases using a variety of techniques. For example, in supermarkets, we can discover the items that are often purchased together and that are hidden within the data. This helps make better decisions which improve the business outcomes. One of the techniques that are used to discover frequent patterns in large databases is frequent itemset mining (FIM) that is a part of association rule mining (ARM). There are different algorithms for mining frequent itemsets. One of the most common algorithms for this purpose is the Apriori algorithm that deduces association rules between different objects which describe how these objects are related together. It can be used in different application areas like market basket analysis, student’s courses selection process in the E-learning platforms, stock management, and medical applications. Nowadays, there is a great explosion of data that will increase the computational time in the Apriori algorithm. Therefore, there is a necessity to run the data-intensive algorithms in a parallel-distributed environment to achieve a convenient performance. In this paper, optimization of the Apriori algorithm using the Spark-based cuckoo filter structure (ASCF) is introduced. ASCF succeeds in removing the candidate generation step from the Apriori algorithm to reduce computational complexity and avoid costly comparisons. It uses the cuckoo filter structure to prune the transactions by reducing the number of items in each transaction. The proposed algorithm is implemented on the Spark in-memory processing distributed environment to reduce processing time. ASCF offers a great improvement in performance over the other candidate algorithms based on Apriori, where it achieves a time of only 5.8% of the state-of-the-art approach on the retail dataset with a minimum support of 0.75%.

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Section: Related Workmentioning

confidence: 99%

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

Alrahwan,

Farouk

2024

International Journal of Intelligent Systems

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“…In the Reduce function, the key and value values in equation 3.17 are combined to obtain the sequence (key : value 1 , value 2 , ..., value n ) , which is passed to the Reduce function for processing. the Reduce function normalizes the sequence to obtain the set (P c 1, P m 1, P c 2, P m 2, , ...P c i, P m j) which can be derived from the optimized population [18]. the computational flow of the Reduce function is shown in Figure 3.3.…”

Section: Vs Model Based On Improved Qgamentioning

confidence: 99%

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

Yu,

2023

SCPE

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E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

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“…Adopting GA to optimize the PSO algorithm to avoid the combination explosion problem and the problem of early stagnation of algorithm search, it turned out that the number of candidate item sets is reduced effectively and the convergence performance of the algorithm is improved. In order to avoid the combination explosion problem in the study of web service composition, S. Kannimuthu et al [ 25 ] proposed a hybrid genetic algorithm (HGA), which combines quantum operators and classical genetic operators, to mine efficient web service composition. The chromosome constructed by superposition qubits based on quantum computing model achieves good results in terms of running time and memory consumption.…”

Section: Introductionmentioning

confidence: 99%

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China

Wu,

Wen,

Zhu

2024

PLoS ONE

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Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

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Discovery of Interesting Itemsets for Web Service Composition Using Hybrid Genetic Algorithm

Cited by 8 publications

References 56 publications

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China

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