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Enhancing Evolutionary Algorithms With pattern mining for Sparse Large-Scale Multi-Objective Optimization Problems

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1786-1801页

作者： Sheng Qi Rui Wang Tao Zhang Weixiong Huang Fan Yu Ling Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China College of Systems Engineering National University of Defense TechnologyChangsha 410073and also with the Xiangjiang LaboratoryChangsha 410205China College of Transportation and Engineering Central South UniversityChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational ***,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables *** paper is dedicated to addressing the challenges posed by *** start,we introduce innovative objective functions customized to mine maximum and minimum candidate *** substantial enhancement dramatically improves the efficacy of frequent pattern *** this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific ***,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero *** novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function *** extensively tested our algorithm across eight benchmark problems and four real-world *** results demonstrate that our approach achieves competitive solutions across various challenges.

关键词： Evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.

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An Algorithm for mining Gradual Moving Object Clusters pattern From Trajectory Streams

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Computers, Materials & Continua 2019年第6期59卷 885-901页

作者： Yujie Zhang Genlin Ji Bin Zhao Bo Sheng School of Computer Science and Technology Nanjing Normal UniversityNanjing210023China Department of Computer Science University of Massachusetts Boston100 Morrissey BoulevardBostonMAUSA

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and *** the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory *** paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window *** processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters ***,the density peaks clustering algorithm is exploited to identify clusters of different *** stable relationship between relatively few moving objects is used to improve the clustering ***,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is *** relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating ***,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

关键词： Trajectory streams pattern mining moving object clusters pattern discovery of moving clusters pattern

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A Fast Algorithm for mining Top-Rank-k Erasable Closed patterns

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Computers, Materials & Continua 2022年第8期72卷 3571-3583页

作者： Ham Nguyen Tuong Le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Informetrics Research Group Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

The task of mining erasable patterns(EPs)is a data mining problem that can help factory managers come up with the best product plans for the *** problem has been studied by many scientists in recent times,and many approaches for mining EPs have been *** closed patterns(ECPs)are an abbreviated representation of EPs and can be con-sidered condensed representations of EPs without information *** methods of mining ECPs identify huge numbers of such patterns,whereas intelligent systems only need a small number.A ranking process therefore needs to be applied prior to use,which causes a reduction in *** overcome this limitation,this study presents a robust method for mining top-rank-k ECPs in which the mining and ranking phases are combined into a single ***,we propose a virtual-threshold-based pruning strategy to improve the mining *** on this strategy and dPidset structure,we then develop a fast algorithm for mining top-rank-k ECPs,which we call ***,we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS(Top-rank-k Erasable pattern mining Using the Subsume concept),which are state-of-the-art algorithms for mining top-rank-k *** results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.

关键词： pattern mining erasable closed pattern mining top-rank-k pattern mining

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A Parallel High-Utility Itemset mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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CLEAN:Frequent pattern-Based Trajectory Compression and Computation on Road Networks

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China Communications 2020年第5期17卷 119-136页

作者： Peng Zhao Qinpei Zhao Chenxi Zhang Gong Su Qi Zhang Weixiong Rao Tongji University Shanghai 201804China IBM T.J.Watson Research Center New York 10598USA

The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

关键词： trajectory compression pattern mining spatial-temporal compressions range query clustering

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Data mining Based Cyber-Attack Detection

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系统仿真技术 2017年第2期13卷 90-104页

作者： TIANFIELD Huaglory Department of Computer Communications and Interactive Systems Glasgow Caledonian UniversityGlasgow G4 0BAUK

Detecting cyber-attacks undoubtedly has become a big data problem. This paper presents a tutorial on data mining based cyber-attack detection. First,a data driven defence framework is presented in terms of cyber security situational awareness. Then, the process of data mining based cyber-attack detection is discussed. Next,a multi-loop learning architecture is presented for data mining based cyber-attack detection. Finally,common data mining techniques for cyber-attack detection are discussed.

关键词： big data analytics cyber-attack detection cyber security cyber situational awareness data mining pattern mining machine learning

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