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IEEE ACCESS 2020年 8卷 41637-41648页

作者： Yin, Yunfei Long, Lianjie Deng, Xiyu Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data;the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.

关键词： data mining data models Stability analysis dynamics Mathematical model Internet of Things Analytical models Clustering dynamic characteristics dynamic data mining IoT sensors

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Chaotic encryption method for network privacy data based on dynamic data mining 6

Chaotic encryption method for network privacy data based on ...

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6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Feng, Hao Peng, Xiangli Li, Jing Liu, Fen Long, Fei Qiu, Shuang Hubei Elect Power Co Ltd State Grid Informat & Commun Branch Wuhan 430077 Hubei Peoples R China State Grid Hubei Elect Power Co Ltd Wuhan 430077 Hubei Peoples R China

ISBN: (纸本)9781665432634

In order to improve the security of network privacy data, this paper proposes a new chaotic encryption method of network privacy data based on dynamic data mining. In this method, SOM neural network is used for dynamic training and mining of network privacy sample data. Based on the results of dynamic training and mining, the quantitative coding characteristics of network privacy data are obtained by using logistics chaotic mapping. Under the constraint of association mapping, the chaotic encryption results of network privacy data are output to complete the chaotic encryption of network privacy data. Experimental results show that, compared with the traditional chaotic encryption method, the proposed method has higher security factor and wider encryption range, and the highest encryption security factor can reach 0.91. Therefore, this method can effectively ensure the security of network privacy data.

关键词： dynamic data mining network privacy data chaotic encryption

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Stability-based dynamic Bayesian Network method for dynamic data mining

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 77卷 283-310页

作者： Naili, Mohamed Bourahla, Mustapha Naili, Makhlouf Tari, AbdelKamel Univ Bordj Bou Arreridj Fac Math & Informat Dept Comp Sci Bordj Bou Arreridj 34030 Algeria Univ Msila Dept Comp Sci Msila 28000 Algeria Univ Biskra Dept Comp Sci Biskra 07000 Algeria Univ Bejaia Fac Fundamental Sci Lab Med Comp LIMED Bejaia 06000 Algeria

In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model.

关键词： dynamic data mining dynamic model Stability dynamic Bayesian Network Grow-Shrink algorithm Modeling and simulation

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Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments

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INFORMATION SCIENCES 2024年 657卷

作者： Kim, Hyeonmo Kim, Hanju Cho, Myungha Vo, Bay Lin, Jerry Chun-Wei Fujita, Hamido Yun, Unil Sejong Univ Dept Comp Engn Seoul South Korea HUTECH Univ Fac Informat Technol Ho Chi Minh City Vietnam Silesian Tech Univ Fac Automat Control Elect & Comp Sci Gliwice Poland Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur 54100 Malaysia Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Granada Spain Iwate Prefectural Univ Reg Res Ctr Iwate Japan

Various studies on high utility pattern mining have been conducted to satisfy the emerging need to consider the characteristics of real-world databases, such as the importance and quantity of items. In the traditional utility-based framework, the mining result is influenced by the number of items in a pattern, or in some cases, single utilities of items. In order to overcome this drawback, high average utility pattern mining has been proposed. It provides more interesting results since it takes into account the average utility of patterns by considering their lengths. Methods based on this concept have emerged in recent years, including ones that target incremental environments. However, existing algorithms create an enormous number of candidate patterns or require complex operations during the mining process. To address this degradation, we propose a new and more efficient approach for mining high average utility patterns in dynamic environments. The proposed algorithm utilizes a data structure more efficient than previous ones, which takes the form of an indexed list. It also incorporates efficient realigning and mining techniques for handling incremental data and accurately mining results. Experimental results show the superiority of the proposed approach in terms of runtime, memory usage, scalability, and accuracy.

关键词： data mining High average utility patterns dynamic data mining Indexed list based structure Pattern pruning

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Research on data Flow Partitioning Based on dynamic Feature Extraction

Research on Data Flow Partitioning Based on Dynamic Feature ...

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7th International Conference on Communications, Signal Processing, and Systems (CSPS)

作者： Wang, Wei Zhang, Min Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

With the rapid development of the Internet of things, social networks, and e-commerce, the era of big data has arrived. Although big data has great potential for many areas such as industry, education, and health care, getting valuable knowledge from big data can be a daunting task. Big data has the characteristics of high-speed change, and its content and distribution characteristics are in dynamic changes. Most current models are static learning models that do not support online updating, making it difficult to learn dynamically changing big data features in real time. In order to solve this problem, this paper proposed a method to support incremental recursive least squares (IRLS) regression parameter estimation and variable sliding window algorithm to analyze and judge the trends of dynamic characteristics of data streams, which can provide early warning, status assessment, and decision support for monitoring objects and improve the accuracy and adaptability of data flow classification. The real-time computational and analysis accuracy are obviously improved than the traditional algorithm, and the simulation results verify the effectiveness of the proposed algorithm.

关键词： Trend analysis dynamic data mining Incremental recursive least squares method Vaiable sliding window

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Incremental high average-utility itemset mining: survey and challenges

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Chen, Jing Yang, Shengyi Ding, Weiping Li, Peng Liu, Aijun Zhang, Hongjun Li, Tian Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Jiangsu Peoples R China Guizhou Minzu Univ Sch Phys & Mechatron Engn Guiyang 550025 Guizhou Peoples R China Inner Mongolia Univ Sci & Technol Baotou Teachers Coll Baotou 014030 Inner Mongolia Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Jiangsu Peoples R China Nanjing Vocat Univ Ind Technol Sch Comp & Software Nanjing 210003 Peoples R China

The High Average Utility Itemset mining (HAUIM) technique, a variation of High Utility Itemset mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

关键词： dynamic data mining High Utility Item mining High Average Utility Item mining Pattern mining

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A dynamic Dominance-Based Rough Set Approach for Processing Ordered data 1

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International Joint Conference on Rough Sets (IJCRS)

作者： Li, Shaoyong Hong, Zhiyong Wuyi Univ Fac Intelligent Mfg Jiangmen 529020 Peoples R China

ISBN: (数字)9783030228156

ISBN: (纸本)9783030228156;9783030228149

In real-world many information systems are varying over time. How to process efficiently dynamic data is a hot issue in data mining research field. Processing information with preference-ordered attribute domain is an important task in multi-criteria decision making. Since dominance-based rough set approach can process information with preference-ordered attribute domain, it has been applied widely to multi-criteria decision making. In the paper, we introduce a new strategy of updating approximations in DRSA under the cases when a lot of objects being added. For thus, the notation of base boundaries of approximations in DRSA is given to reduce the redundant computation in updating approximations. The feasibility of the approach was validated by a numerical example.

关键词： Rough set Dominance relation dynamic data mining

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dynamics reconstruction and classification via Koopman features

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data mining AND KNOWLEDGE DISCOVERY 2019年第6期33卷 1710-1735页

作者： Zhang, Wei Yu, Yao-Chi Li, Jr-Shin Washington Univ St Louis MO 63130 USA

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and biology to medicine. Tools from machine learning, data mining, and neurocomputing have been extensively explored and utilized to accomplish such compelling data analytics tasks. However, for time-series data presenting active dynamic characteristics, many of the state-of-the-art techniques may not perform well in capturing the inherited temporal structures in these data. In this paper, integrating the Koopman operator and linear dynamical systems theory with support vector machines, we develop a novel dynamic data mining framework to construct low-dimensional linear models that approximate the nonlinear flow of high-dimensional time-series data generated by unknown nonlinear dynamical systems. This framework then immediately enables pattern recognition, e.g., classification, of complex time-series data to distinguish their dynamic behaviors by using the trajectories generated by the reduced linear systems. Moreover, we demonstrate the applicability and efficiency of this framework through the problems of time-series classification in bioinformatics and healthcare, including cognitive classification and seizure detection with fMRI and EEG data, respectively. The developed Koopman dynamic learning framework then lays a solid foundation for effective dynamic data mining and promises a mathematically justified method for extracting the dynamics and significant temporal structures of nonlinear dynamical systems.

关键词： Koopman operators dynamic data mining data-driven methods Dimensionality reduction Spectral methods Time-series classification Healthcare Bioinformatics

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Application of dynamic data mining and Case-based Reasoning in The Drilling Fault Diagnosis

Application of Dynamic Data Mining and Case-based Reasoning ...

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Li, Qi Zhai, Peng Zhao, Yunli Xian Shiyou Univ Inst Petr Engn Xian 710065 Shannxi Peoples R China China Petr Pipeline Inspect Technol Co Ltd Langfang 065000 Hebei Peoples R China

ISBN: (纸本)9783037854174

Most of the traditional drilling fault diagnosis & decision systems use static data mining technology, so the update of knowledge base becomes its bottlenecks in its development. In order to meet the actual needs, this paper puts forward the method, which combines dynamic data mining technology with case-based reasoning technology, to design drilling fault diagnosis & decision systems. First, design drilling fault diagnosis system overall, then describe the realization of how to realize dynamic data mining and case-based reasoning in detail, finally, introduce some question about the update of knowledge base.

关键词： dynamic data mining Case-based Reasoning Drilling Fault Diagnosis Update of Knowledge Base

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dynamic data mining Approach to WMRHM

Dynamic Data Mining Approach to WMRHM

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IEEE Conference on Industrial Electronics and Applications

作者： Dipti Durgesh Patil Vijay M. Wadhai Dept. Computer Engineering MAEER's MITCOE Principal MAEER's MITCOE

ISBN: (纸本)9781457721182

Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Our approach focused towards handling all kinds of vital signals like ECG, EMG, SpO2 etc. which previous work was not supporting. While predictions the framework considers all parameters like patient history, domain expert's rules and continuously monitored real-time signals. Implementation and results of applying clustering algorithms (Graph theoretic, K-means) on patient's historical health data for forming the health rule base are discussed here. The framework has been designed to perform the analysis on the instantaneous and stream (continuous) data over a sliding time window which applies dynamic data mining on the live data. The comparative analysis on vital signals made from various clustering algorithms adds extra dimension to global risk alerts and help doctors to diagnose more accurately.

关键词： dynamic data mining Clustering Vital signal Processing Wireless Sensor Networks

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