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The cleaning method of duplicate big data based on association rule mining algorithm

INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第2期16卷 220-231页

作者： Wu, Ming Hunan City Univ Coll Informat & Elect Engn Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of low cleaning efficiency and serious memory consumption in traditional large data cleaning methods, this paper proposes a new cleaning method of repeated big data based on association rule mining algorithm. This method uses association rule mining algorithm to obtain the frequent itemsets of repeated big data after repeated cycle calculation. At the same time, the output mode of the algorithm is optimised in parallel, and the Hadoop interface is modified to change the reading mode of MapReduce. The first frequent itemset is used to clean the repeated big data. The experimental results show that the proposed method can effectively reduce the execution time and memory consumption, and the shortest cleaning time is only 1.28 min, indicating the feasibility of the proposed method.

关键词： low cleaning efficiency serious memory consumption association rule mining algorithm duplicate big data cleaning frequent items

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Research on the Application of association rule mining algorithm on Large Datasets

Research on the Application of Association Rule Mining Algor...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Li, Jilin Wang, Man Shandong Institute of Commerce and Technology Sandong Jinan China

ISBN: (纸本)9798350383348

With the advent of the era of big data, the amount of data we are facing is increasing exponentially. In this context, how to effectively deal with and analyze these large-scale data integration is an important research topic. As a powerful tool to find interesting relationships among data items in large data sets, association rule mining algorithm has been paid more and more attention. This paper will deeply discuss the role of association rule mining algorithm in large data sets. © 2024 IEEE.

关键词： association rule mining algorithm confidence theory Data analysis and positioning Data applications intelligent analysis large data sets

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Efficient association rule mining algorithm Based on User Behavior for Cloud Security Auditing

Efficient Association Rule Mining Algorithm Based on User Be...

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IEEE International Conference of Online Analysis and Computing Science (ICOACS)

作者： Zhao, Chunye Tu, Shanshan Chen, Haoyu Huang, Yongfeng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Int Relat Sch Informat Sci & Technol Beijing 100091 Peoples R China Beijing Univ Post & Telecommun Int Sch Beijing 100876 Peoples R China

ISBN: (纸本)9781467377553

Cloud Storage provides external data storage service by combing and coordinating different types of storage devices in the network, so that they can collectively work together. However it always exists a trust game relationship between users and service providers, therefore building a healthy, fair and secure cloud data service environment is necessary, especially for the security auditing on the state of cloud data and operation processes. This paper proposes user behavior (UB)-based scheme, by analyzing log data from cloud servers. Firstly, we present the description rules of UB for operating system logs. Then we put forward an association rule mining algorithm based on the Long Sequence Frequent Pattern (LSFP) to extract UB. At last, the result of experiment proves that our solution can implement the track and forensic of data leakage efficiently for the cloud security auditing.

关键词： Cloud security auditing User behavior analysis association rule mining algorithm Data track and forensic

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Efficient association rule mining algorithm Based on User Behavior for Cloud Security Auditing

Efficient Association Rule Mining Algorithm Based on User Be...

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2016 IEEE International Conference of Online Analysis and Computing Science(ICOACS 2016)

作者： Chunye Zhao Shanshan Tu Haoyu Chen Yongfeng Huang Department of Electronic Engineering Tsinghua University School of Information Science and Technology University of International Relation International School Beijing University of Post and Telecommunication

Cloud Storage provides external data storage service by combing and coordinating different types of storage devices in the network,so that they can collectively work *** it always exists a trust game relationship between users and service providers,therefore building a healthy,fair and secure cloud data service environment is necessary,especially for the security auditing on the state of cloud data and operation *** paper proposes user behavior(UB)-based scheme,by analyzing log data from cloud ***,we present the description rules of UB for operating system *** we put forward an association rule mining algorithm based on the Long Sequence Frequent Pattern(LSFP) to extract *** last,the result of experiment proves that our solution can implement the track and forensic of data leakage efficiently for the cloud security auditing.

关键词： Cloud security auditing User behavior analysis association rule mining algorithm Data track and forensic

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Research on Student Behavioral Advanced Warning Based on Campus-Card Consumption Data 27

Research on Student Behavioral Advanced Warning Based on Cam...

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27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： He, Qinhan Liu, Jinhui Zhou, Guangfei Gao, Mingyu Xidian Univ Sch Artificial Intelligence Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Aeronaut Comp Tech Res Inst Xian Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China

ISBN: (纸本)9798350391961;9798350391954

The frequent occurrence of campus security incidents has alerted the security management of university. With the development of campus informational management, using big data to carry out student behavior analysis is increasingly favored by campus management because of strong timeliness and precision data sources. Nowadays, some universities in China are adopting informational method to analyze students' behavior in university, and the scope of application has been gradually promoted, but there is little experience in the research of campus security warning. In order to solve the application of big data mining technology in the special advanced warning of campus security, this paper proposes to establish student's behavioral model based on their consumption data on campus, and apply association rule mining algorithms and MongoDB data storage technology to advanced warning analysis of students' abnormal behaviors, so as to provide more important data support for campus security management. Enable managers to easily identify security risks and precaution safety management comprehensively. Furthermore, visualization analysis is also used to promote the efficiency of campus security management.

关键词： association rule mining algorithm Campus-Card consumption data Behavioral advanced warning

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Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

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IET COMMUNICATIONS 2020年第7期14卷 1192-1197页

作者： Safara, Fatemeh Souri, Alireza Serrizadeh, Masoud Islamic Azad Univ Islamshahr Branch Dept Comp Engn Islamshahr Iran Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr Iran

Nowadays, detecting anomaly events in communication networks is highly under consideration by many researchers. In a large communication network, traffic is massive, which leads to a larger amount of data travelling and also the growth of noise. Therefore, to extract meaningful data for anomaly detection would be very challenging. Each attack has its own behaviour that determines the type of attack. However, some attacks may have similar behaviours and only differ in some features. Extracting such meaningful features is of special importance. In this study, an association rule mining algorithm, in particular, the Apriori algorithm is employed to extract appropriate features from the raw data including rules and repetitive patterns. The extracted features would be used then for classifying the data and detecting anomalies in communication networks. A hybrid of artificial neural network and AdaBoost classification algorithms are employed for classifying the detected events with normal behaviour and attack events. The proposed method is compared with previous methods reported in this field such as CART, CHAID, multiple linear regression and logistic regression on KDDCUP99 data set. The results showed that the proposed method outperformed other classifiers examined. The strategy of reinforcement learning is used to combine the classifier's results which is based on Max vote strategy.

关键词： learning (artificial intelligence) pattern classification feature extraction regression analysis data mining security of data neural nets improved intrusion detection method communication network artificial neural networks anomaly events data travelling anomaly detection association rule mining algorithm artificial neural network detected events attack events AdaBoost classification algorithm KDDCUP99 dataset

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Studying Fine-Grained Co-Evolution Patterns of Production and Test Code 14

Studying Fine-Grained Co-Evolution Patterns of Production an...

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14th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM)

作者： Marsavina, Cosmin Romano, Daniele Zaidman, Andy Delft Univ Technol NL-2600 AA Delft Netherlands

ISBN: (纸本)9780769553047

Numerous software development practices suggest updating the test code whenever the production code is changed. However, previous studies have shown that co-evolving test and production code is generally a difficult task that needs to be thoroughly investigated. In this paper we perform a study that, following a mixed methods approach, investigates fine-grained co-evolution patterns of production and test code. First, we mine fine-grained changes from the evolution of 5 open-source systems. Then, we use an association rule mining algorithm to generate the co-evolution patterns. Finally, we interpret the obtained patterns by performing a qualitative analysis. The results show 6 co-evolution patterns and provide insights into their appearance along the history of the analyzed software systems. Besides providing a better understanding of how test code evolves, these findings also help identify gaps in the test code thereby assisting both researchers and developers.

关键词： data mining program testing public domain software software engineering association rule mining algorithm coevolving test fine-grained coevolution patterns mixed methods approach open-source systems production code software development practices test code association rules Educational institutions Production Software systems Statistical analysis Testing test codes Software systems association rules open source software program testing Educational institutions Software engineering data mining Software design computer software

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Optimizing mining association rules based on Artificial Neural Network

Optimizing Mining Association Rules based on Artificial Neur...

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World Automation Congress (WAC)

作者： Liu Tiecheng Beihua Univ Coll Informat Technol Jilin Jilin Peoples R China

ISBN: (纸本)9781467344975

The basic function of a neural system is to intelligent learning from specific examples known as neurons. It has great pattern adaptive capability that may be used to judge between old model and well model. Neural systems have many characteristics such as autonomous, uniqueness, recognition of foreigners, noise tolerance, and distributed detection. Inspired by neural network system, Artificial Neural Network has emerged during the last decade. It is incited by many researchers to build, study, and design neural-based models for a variety of application regions. Artificial neural system can be defined as adaptive model that is inspired by neural network, observed neural functions, mechanisms and principles. association rule mining is one of well researched and the most important techniques of datum mining. The purpose of association rules is to refine interesting correlations, associations, frequent patterns, or casual constructions in sets of aims in other datum repositories or the transaction databases. association rule is widely used in various regions such as telecommunication network, inventory control, intelligent decision, risk management and market analysis etc. Artificial Neural Network is the most widely used algorithm for mining the association rules. In this paper, Artificial Neural Network is studied and optimized based on classification system. The performance of the ANN based on classification system is evaluated by varying number of generations and computing accuracy at different factors. Three standard datum had been used to computer the accuracy. The test result shows that the system can give highest accuracy more than o.4.

关键词： Neural network association rule mining algorithm confidence and support counting classification system

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DHPTID-HYBRID algorithm: A Hybrid algorithm for association rule mining

DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association ...

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6th International Conference on Advanced Data mining and Applications (ADMA)

作者： Sonawani, Shilpa Mishra, Amrita JSS Acad Tech Educ Dept Comp Sci & Engn Noida Uttar Pradesh India

ISBN: (纸本)9783642173158

Direct Hashing and Pruning algorithm of ARM performs well at initial passes by smaller candidate 2-itemset generation and turns out to be very powerful to facilitate initial itemset generation. Efficient pruning technique of AprioriTid algorithm is highly effective for frequent itemset generation in the later passes. Theoretical and practical studies reveals the underneath facts for the scope of improvement for both the algorithms. This paper describes a hybrid approach for association rule mining, based on coupling of best part of two well known ARM algorithms, AprioriTid and DHP with a binary approach which significantly improves the performance.

关键词： association rule mining algorithm DHP AprioriTid binary approach coupled algorithm

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