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Hybridized optimization oriented fast negative sequential patterns mining

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5279-5303页

作者： Yadav, Poonam DAV Coll Engn & Technol Kanina 123027 Haryana India

Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative correlations between item sets, which are overlooked by positive sequential pattern mining (PSP) (for instance, utilization of medical service). Yet, discovering the NSP is very complex than finding PSP because of the important problem complexity occurred by high computational cost, non-occurring elements, as well as huge search space in evaluating NSC, and most of the NSP based existing works are inefficient. Therefore, this paper intends to propose a fast NSP mining algorithm for the disease prediction model. This model includes Data normalization, Data separation based on labels, and pattern recognition phases. In the midst of data separation, the maximum occurring data is optimally selected using a new algorithm that hybridizes the FireFly (FF) algorithm and Grey Wolf Optimization (GWO). This proposed Firefly induced Grey Wolf optimization (F-GWO) algorithm automatically selects the maximum occurring information as per the PSP support. The proposed model is compared over other conventional methods with varied measures. Especially, the computation cost of our model is 46.87%, 6.27%, 9.37%, 2.76%, and 66.62% better than the existing GA, ABC, PSO, FF, and GWO models respectively.

关键词： positive sequential pattern Negative sequential pattern Optimization algorithm GWO FF

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Mining Negative sequential Rules from Negative sequential patterns 27th

Mining Negative Sequential Rules from Negative Sequential Pa...

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Sun, Chuanhou Jiang, Xiaoqi Dong, Xiangjun Xu, Tiantian Zhao, Long Li, Zhao Zhao, Yuhai Qilu Univ Technol Shandong Acad Sci Dept Comp Sci & Technol Jinan Peoples R China Northeastern Univ Shenyang Peoples R China

ISBN: (纸本)9783031001239;9783031001222

As an important tool for behavior informatics, negative sequential rules (NSRs, e.g., double right arrow inverted left perpendicular) are sometimes much more informative than positive sequential rules (PSRs, e.g., double right arrow ), as they can provide valuable decision-making information from both negative and positive sides. Very limited NSR mining algorithms are available now and most of them discover NSRs only from positive sequential patterns (PSPs, e.g., ) rather than from negative sequential patterns (NSPs, e.g., ), which may result in a loss of important information. However, discovering NSRs (e.g., double right arrow) from NSPs is much more difficult than mining NSRs from PSPs because NSPs do not satisfy the downward closure property. In addition, it is very difficult to find which kind of NSRs should be generated. This paper proposes a novel algorithm named nspRule to address all these difficulties. The experiment results on real-life and synthetic datasets show that nspRule can mine NSRs correctly and efficiently w.r.t. several aspects including rule number, runtime, memory usage and scalabilit.

关键词： Negative sequential rule Negative sequential pattern positive sequential rule positive sequential pattern

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Research on typical algorithms in negative sequential pattern mining

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Open Automation and Control Systems Journal 2015年第1期7卷 934-941页

作者： Gong, Yongshun Liu, Chuanlu Dong, Xiangjun School of Information Qilu University of Technology JinanShandong250353 China

Negative sequential pattern (NSP), which contains both non-occurring and occurring items, can discover much more interest roles than positive sequential pattern (PSP) in many applications. NSP mining, however, has been just caught attention and very limited methods are available to mine NSP. Furthermore, there is not a unified definition about negative containment, i.e., how a data sequence contains a negative sequence. The researchers who begin to study negative sequential pattern are often confused by these different definitions and are eager to know the differences of the existing methods. So in this paper, we select four typical existing methods, PNSP, Neg-GSP, e-NSP and NSPM, implement their algorithms by JAVA, and compare their definition, method, runtime and the number of NSPs. Examples and experiments on four dataset clearly show their differences. © Bu et al.

关键词： Negative containment Negative sequential pattern positive sequential pattern

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Comparisons of Typical Algorithms in Negative sequential pattern Mining

Comparisons of Typical Algorithms in Negative Sequential Pat...

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IEEE Workshop on Electronics, Computer and Applications (IWECA)

作者： Dong, Xiangjun Gong, Yangshun Zhao, Lulin Qilu Univ Technol Sch Informat Jinan 250353 Peoples R China

ISBN: (纸本)9781479945658

Negative sequential pattern (NSP), which contains both non-occurring and occurring items, can play more important roles than positive sequential pattern (PSP) in many applications. NSP mining, however, has been just caught attention and very limited methods are available to mine NSP. Furthermore, there is not a unified definition about negative containment, i.e., how a data sequence contains a negative sequence. So in this paper, we select three typical existing methods, PNSP, Neg-GSP and e-NSP, and compare their definitions, runtime and the number of NSPs. Examples and experiments on same dataset clearly show their differences.

关键词： positive sequential pattern negative sequential pattern negative containment

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