检索结果-内蒙古大学图书馆

Research on deep mining of MOOC multimodal resources based on improved eclat algorithm

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2023年第1期33卷 99-113页

作者： Cao, Yu Chen, Shu-Wen Wang, Yu-Xi Jiangsu Second Normal Univ Sch Math & Informat Technol Nanjing 210013 Peoples R China

In order to overcome the problems of low recall and precision in traditional MOOC multimodal resource mining methods, this paper proposes a new MOOC multimodal resource deep mining method based on improved eclat algorithm. Based on cloud computing technology, according to MOOC resource pool structure, MOOC multi-modal knowledge map is constructed, and hash chain is used to analyse the attribute connection rules between knowledge maps. Based on the attribute connection rules, the improved eclat algorithm is used to transform the captured modal information of resources, so as to design the MOOC multi-modal resource deep mining process and get the results of resource deep mining. The experimental results show that the recall and precision of this method are above 97%, the mining effect is better, and the mining time is always less than 0.7 s, the mining efficiency is higher, and the actual application effect is better.

关键词： modal mining association rules MOOC resources eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Discovering Frequent Itemsets Over Event Logs Using eclat algorithm

Discovering Frequent Itemsets Over Event Logs Using ECLAT Al...

引用

International Conference on Advances in Soft Computing, Intelligent Systems and Applications (ASISA)

作者： Sundeep, A. S. Veena, G. S. MS Ramaiah Inst Technol Bengaluru India

ISBN: (纸本)9789811052729;9789811052712

Event logs are files that can record significant events that occur on a computing device. For example, when a user logs in or logs out, when the device encounters an error, etc., events are recorded. These events logs can be used to troubleshoot the device when it is down or works inappropriately. Generally, automatic device troubleshoot includes mining interesting patterns inside log events and classifying them as normal patterns or anomalies. In this paper, we are providing a sequential mining technique named eclat to discover interesting patterns over event logs.

关键词： Event logs eclat algorithm Sequence patterns

来源：评论

学校读者我要写书评

暂无评论

Parteclat: an improved eclat-based frequent itemset mining algorithm on spark clusters using partition technique

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第6期25卷 4463-4480页

作者： Raj, Shashi Ramesh, Dharavath Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

Frequent itemset mining (FIM) is one of the prominent techniques to extract knowledge from transactional databases. Finding frequent itemsets becomes tiresome while dealing with large-scale datasets due to the enormous demand for computational resources. Cluster-based versions of FIM algorithms become a natural choice to make FIM algorithms efficient and scalable for large-scale datasets and to meet the ever-growing data needs. A variety of MapReduce-based algorithms on the Hadoop cluster has been developed to meet the expectations. Due to the iterative nature of the FIM algorithms, they are still unable to perform adequately. Bottlenecks associated with MapReduce-based FIMs include challenges originating from adapting FIM algorithms in parallel and distributed contexts, as well as reading and publishing intermediate results to HDFS, which necessitates significant disc and communication I/O. Many FIM algorithms have been redesigned on Spark to achieve efficiency for large-scale datasets by utilizing its in-memory processing capabilities. However, Spark-based FIM adaptations still face the same challenges except achieving memory efficiency. The limitations associated with basic FIM algorithms, such as repeated scanning of input data, massive candidate generation, and maintaining large conditional trees in memory, still need to be addressed. Also, tuning of Spark's shuffle behavior becomes important to control the communication overhead. In this paper, we propose a Spark-based algorithm, namely Parteclat. It uses the eclat method in combination with the partitioning technique to gain efficiency and scalability. Vertical data format helps to avoid repeated scanning of input data to calculate individual support values. It utilizes the benefits of the partitioning technique to limit the movement of key-value pairs across the cluster nodes (shuffle overheads) during iterations. Also, it helps to deal efficiently with the memory requirements to handle large Transaction ID

关键词： Apache Spark Frequent itemset mining eclat algorithm Large scale datasets

来源：评论

学校读者我要写书评

暂无评论

Mining fault association rules in the perception layer of electric power sensor network based on improved eclat 17

Mining fault association rules in the perception layer of el...

引用

17th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Zhao, Yongsheng Lv, Yuxiang Zeng, Jian Dong, Yawen Fang, Honglin Yu, Peng Xu, Siya Anhui Jiyuan Software Co LTD Hefei Peoples R China State Grid Sichuan Elect Power Co Econ & Tech Res Inst Neijiang Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728186160

Aiming at the problem that existing association rule mining algorithms cannot quickly mine faulty association rules in the current perception layer of electric power sensor networks, an improved eclat mining algorithm fast_eclat is proposed. The algorithm combines the characteristics of sparse data and large number of transactions at the perception layer of the power sensor network, and adopts a set intersection strategy based on pruning cross-counting, which reduces the computational complexity and improves the computational efficiency of the algorithm, which can more effectively deal with fault association rules. Comparative analysis through simulation experiments shows that the fast_eclat algorithm has better performance in the face of sparse data and large number of transactions.

关键词： Power sensor network Data mining Association rules eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Improvement of eclat algorithm Based on Support in Frequent Itemset Mining

Improvement of Eclat Algorithm Based on Support in Frequent ...

引用

The 6th International Conference on Computer Research and Development

作者： Xiaomei Yu Hong Wang School of Information Science and Engineering Shandong Provincial Key Laboratory for Distributed Computer software Novel TechnologyShandong Normal University

Finding frequent itemsets is computationally the most expensive step in association rules mining, and most of the research attention has been focused on it. With the observation that support plays an important role in frequent item mining, in this paper, a conjecture on support count is proved and improvements of traditional eclat algorithm are presented. The new Bi-eclat algorithm sorted on support: Items sort in descending order according to the frequencies in transaction cache while itemsets use ascending order of support during support count. Compared with traditional eclat algorithm, the results of experiments show that the Bi-eclat algorithm gains better performance on several public databases given. Furthermore, the Bi-eclat algorithm is applied in analyzing combination principles of prescriptions for Hepatitis B in Traditional Chinese Medicine, which shows its efficiency and effectiveness in practical usefulness.

关键词： frequent itemset support count equivalence class eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

引用

NEURAL COMPUTING & APPLICATIONS 2020年第1期32卷 151-161页

作者： Vimal, S. Kalaivani, L. Kaliappan, M. Suresh, A. Gao, Xiao-Zhi Varatharajan, R. Natl Engn Coll Dept IT Kovilpatti Tamil Nadu India Natl Engn Coll Dept EEE Kovilpatti Tamil Nadu India Nehru Inst Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India Univ Eastern Finland Sch Comp Kuopio Finland Sri Ramanujar Engn Coll Dept Elect & Commun Engn Kovilpatti Tamil Nadu India

The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.

关键词： Machine learning Wireless communication Cognitive radio networks Byzantine attack eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Incremental Fuzzy Association Rule Mining for Classification and Regression

引用

IEEE ACCESS 2019年 7卷 121095-121110页

作者： Wang, Ling Ma, Qian Meng, Jianyao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China

The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can only be applied in classification. This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems. First, the sliding window is adopted to divide the fuzzy dataset. Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining. Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy eclat algorithm. When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated;otherwise, the original rule set is still applied. The weights of the rules are calculated to find the evolving relationship. The simulation result shows that this algorithm can improve accuracy and efficiency.

关键词： Fuzzy association rules incremental classification regression eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Identifying the most significant input parameters for predicting district heating load using an association rule algorithm

引用

JOURNAL OF CLEANER PRODUCTION 2020年 275卷 122984-122984页

作者： Liu, Yanfeng Hu, Xiaoxue Luo, Xi Zhou, Yong Wang, Dengjia Farah, Sleiman Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol Sate Key Lab Green Bldg Western China Xian 710055 Peoples R China Univ South Australia Barbara Hardy Inst Sch Informat Technol & Math Sci Adelaide SA Australia

High-energy consumption of a district heating system is mainly caused by low-efficiency operation. Real-time regulation and control of the system in the operation stage has great energy-savings potential, and accurate load prediction is the basis of system control and on-demand supply. The key for obtaining an accurate hourly heating load forecast value is to select reasonable input parameters. Therefore, this paper summarizes the methods of predicting heating loads and selecting input parameters, proposes the eclat algorithm of association rules to obtain the best combination of input parameters, and introduces the Spearman parameter selection method for comparison;the eclat-Support Vector Regression (E-SVR) and Spearman-Support Vector Regression (S-SVR) prediction models are established. The heating load data of a district in Xi'an, China is taken as an example, and the results show that the water supply temperature of the historical primary network was the most significant factor that affects the district ultrashort-term heating load prediction. The E-SVR prediction model has better performance than S-SVR with an R-2 value of 0.92, an accuracy improvement of 8.2%, and a root mean square error (RMSE) reduction of 28.1%. Single factor analysis of different prediction models showed that with an increase in the length of the prediction cycle, the influences of the historical water supply temperature and flow rate on the heating load decrease gradually, and the influence of outdoor temperature increases gradually. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Heating load prediction Input parameter selection method Support vector regression eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

Implementation of Association Rule Mining algorithms on Distributed Data Processing Platforms 4

Implementation of Association Rule Mining Algorithms on Dist...

引用

4th International Conference on Computer Science and Engineering (UBMK)

作者： Seaver, Duygu Tuna, Sabah Aktas, Mehmet S. Kalipsiz, Oya Kanli, Alper Nebi Turgut, Umut Orcun Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey Cybersoft Ar Ge Merkezi Istanbul Turkey

ISBN: (纸本)9781728139647

Association rule mining algorithms are a frequently used data mining tecnique. It is aimed to find the items that are frequently found from the data. Nowadays, large data processing and analysis platforms are not focused on data mining, so they do not offer large-scale libraries for association rule mining algorithms. In the scope of this research, a library has been developed for association rule mining algorithms on a large data processing platform. The Apache Spark platform has been preferred in terms of common usage for the research case study. Implementation methods of different algorithms have been implemented on this platform to benefit from the Map Reduce programming model. In this context, Apriori, eclat and Pascal algorithms are implemented for large data platform. The library created by the implementation method we suggest is comparatively analyzed in terms of performance metrics on big data processing platforms with single and multiple nodes. The methods implemented within the scope of the research are also compared with the performance of the FpGrowth algorithm implemented by the Spark platform. The results of our research show that when tested on large scale data, the Apriori algorithm gives much better performance values than the other algorithms when switching from single-node cluster environment to multi-node cluster environment.

关键词： Association Rule Mining algorithms Big Data Processing and Analysis Platfoms Apriori algorithm eclat algorithm Pascal algorithm Spark Platform

来源：评论

学校读者我要写书评

暂无评论

Research on Association Rules algorithm Based on Bit Storage and Deep Pruning 2

Research on Association Rules Algorithm Based on Bit Storage...

引用

2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Chen Yingcong Li Qiang Tian Tian Lin Maosong Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728108315

Association rule mining is an important part of data mining and is widely used in many fields. Mining frequent itemsets is the most important step and technology. In this paper, the eclat algorithm in the frequent itemset mining algorithm is taken as the research point. In order to solve the problem that the eclat algorithm increases the size of the itemset, causing the vertical list to store transaction records and intersection operations, etc, consumes a lot of time and memory. From the algorithm itself and storage mechanism considerations, a DBeclat (Deep Bit eclat) algorithm based on bit storage mechanism and deep pruning strategy is proposed. The algorithm stores the vertical data list in a binary form that reduces memory consumption, reduces the time consumption of the intersection operation by using the AND operation of the binary data, and combines the multi-angle deep pruning strategy to compress the size of the candidate set. The experimental results show that the proposed algorithm has higher time efficiency and lower memory consumption than the original algorithm.

关键词： association rules bit storage depth pruning eclat algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：