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association rule mining and Its Application

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The Journal of China Universities of Posts and Telecommunications 2001年第4期8卷 13-17页

作者： DUAN Yun feng, LI Jian wei, SONG Jun de, SHU Hua ying (Beijng University of Posts and Telecommunications, Beijing 100876, P.R. China) Beijng University of Posts and Telecommunications Beijing 100876 P.R. China

Several algorithms in data mining technique have been studied recently, among which association is one of the most important techniques. In this paper, we introduce theory of association rule in data mining, and analyze the characteristics of postal EMS service. We create a data warehouse model for EMS services and give the procedure of applying association rule mining based on it. In the end, we give an example of the whole mining procedure. This EMS Data warehouse model and association rule mining technique have been applied in a practical Postal CRM System.

关键词： association rule mining data mining data warehouse data mart fact table frequency item set EMS post

association rule mining for the Infrared Countermeasure by the PF-Growth Algorithm

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Association Rule Mining for the Infrared Countermeasure by t...

第37届中国控制会议

作者： Xu Yang Fang Yang-Wang Wu You-Li Zhang Dan-Xu Huang Chen Department of Aeronautics Engineering Air Force Engineering University 95972 Army

To explore the main influence factors in the infrared countermeasure and reveal the effects of the combinations of the influence factors, this study provides a heuristic idea by adopting the association rule mining theory. First of all, an engagement model including the target model, flare model and missile model is constructed to show different attack situations and countermeasure modes. Meanwhile, a counter-countermeasure algorithm denoted overlap effect is proposed as a recognition approach for distinguishing the true target from the target-flare mixed signal. Then, in view of the miss distance, we separate the association rules into outer and inner levels for mining the relations between the miss distance and the countermeasure ***, FP-growth algorithm is introduced to unearth the association rules by using the off-line data. Finally, we thoroughly investigate the association rules and disclose the main influence factors through simulation examples.

关键词： Infrared countermeasure association rule mining overlap effect FP-growth algorithm miss distance

Quantum Algorithm for mining Frequent Patterns for association rule mining

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Journal of Quantum Information Science 2023年第1期13卷 1-23页

作者： Abdirahman Alasow Marek Perkowski Department of Electrical and Computer Engineering Portland State University Portland OR USA

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

关键词： Data mining association rule mining Frequent Pattern Apriori Algorithm Quantum Counter Quantum Comparator Grover’s Search Algorithm

Privacy-Preserving Collaborative association rule mining

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Privacy-Preserving Collaborative Association Rule Mining

第四届电子商务国际会议

作者： Stan Matwin Nathalie Japkowicz School of Information Technology ＆ Engineering University of Ottawa Canada School of Information Technology ＆ Engineering University of Ottawa Canada

＜正＞ In recent times, the development of privacy technologies has promoted the speed of research on privacy-preserving collaborative data mining. People borrowed the ideas of secure multi-party computation and developed secure multi-party protocols to deal with privacy-preserving collaborative data mining problems. Random perturbation was also identified to be an efficient estimation technique to solve the problems. Both secure multi-party protocol and random perturbation technique have their advantages and shortcomings. In this paper, we develop a new approach that combines existing techniques in such a way that the new approach gains the advantages from both of them.

关键词： Privacy data mining association rule mining secure multi-party computation.

A Novel association rule mining Based on Immune Computational intelligence

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A Novel Association Rule Mining Based on Immune Computationa...

2010 International Conference on Life System Modeling and Simulation & 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment

作者： Xuesong Xu ,Sichun Wang Institute of Management Engineering, Information College of Hunan University of Commerce Changsha,China.

By inspiration of immune computational intelligence, a novel association rule mining algorithm based immune clonal and cluster was proposed. Aim at the efficiency problem of association rules mining,raw data is regarded as antigen and candidate pattern is regarded as antibody. enhancing the antibody's affinity maturation rate and improving the support of candidate patterns through the cluster competition operation. The simulation and real application illustrate this algorithm can increase the convergence velocity and advance veracity of the association rule, and has the remarkable quality of the global and local research reliability.

关键词： association rule mining cluster and competition clonal selection data mining

E-commerce Order Batching Algorithm based on association rule mining in the Era of Big Data

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E-commerce Order Batching Algorithm based on Association Rul...

第30届中国控制与决策会议

作者： Yafang Li Yisong Li School of Economics and Management Beijing Jiaotong University

In E-commerce environment, consumers have higher requirement on rapid order processing, therefore, how to optimize the efficiency of picking operation in order to shorten the order processing time is the emphasis and difficulty for warehouse management of E-commerce enterprises. E-commerce orders are in small batch and high frequency, therefore, the order batching strategy is more suitable to optimize the picking operation efficiency. In the era of big data, the E-commerce enterprises need to face massive customer orders, which makes it necessary for the E-commerce enterprises to adopt big data analysis methods such as data mining to process orders and design the batching algorithm. In this paper, massive E-commerce order data is processed based on the association rule mining Algorithm FP-growth,the correlation degree of orders is established and order batching algorithm FPGB is designed. The results of the experiment demonstrate the effectiveness of FPGB. By adopting FPGB, order batching can be completed in seconds and order batching time can be largely enhanced, which realizes the optimization process of E-commerce order batching strategy and warehouse picking operation in the era of big data.

关键词： Big Data association rule mining E-commerce Order Batching

mining association rule efficiently based on data warehouse

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Journal of Central South University 2003年第4期10卷 375-380页

作者：陈晓红赖邦传罗铤 Changsha 410083 China School of Business School of Business Central South University

The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.

关键词： data mining association rule mining complete association rule set least association rule set

Unlocking new potential of clinical diagnosis with artificial intelligence:Finding new patterns of clinical and lab data

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World Journal of Diabetes 2024年第3期15卷 308-310页

作者： Pradeep Kumar Dabla Department of Biochemistry Govind Ballabh Pant Institute of Postgraduate Medical Education and ResearchDelhi 110002India

Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)*** are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle ***-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician *** medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful *** technological advancements enhance the computational analyses for both research and clinical *** plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor *** potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy ***,an AI ethics framework is essential to guide its responsible *** AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.

关键词： Laboratory medicine Artificial intelligence Machine learning association rule mining Cardiovascular diseases

Discovering hidden patterns:association rules for cardiovascular diseases in type 2 diabetes mellitus

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World Journal of Methodology 2024年第2期14卷 97-106页

作者： Pradeep Kumar Dabla Kamal Upreti Dharmsheel Shrivastav Vimal Mehta Divakar Singh Department of Biochemistry Govind Ballabh Pant Institute of Postgraduate Medical Education and ResearchDelhi 110002India Department of Computer Science CHRISTGhaziabad 201003India Department of Cardiology Govind Ballabh Pant Institute of Postgraduate Medical Education and ResearchDelhi 110002India Barkatullah University Institute of Technology Barkatullah UniversityBhopal 462026India

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical *** aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these *** leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient *** To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern *** This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each *** participants were enrolled from the Cardiology IPD&OPD for the sample *** study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original *** The clinical dataset comprised 35 attributes from enrolled *** analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for *** patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar *** ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease *** The application of this method holds promise for healthcare practitioners to offer valuable insights for enhanc

关键词： Coronary artery disease Type 2 diabetes mellitus Coronary angiography association rule mining Artificial intelligence