检索结果-内蒙古大学图书馆

作者： Xingyue Li Jun Lu School of Computer Science and Technology Heilongjiang University

This paper proposes a fault-tolerant frequent itemset mining algorithm（FTTlist）based on the linear table when the fault-tolerance is 1. The algorithm uses the method of concatenating 1 in the highest bit of the binary number of the known fault-tolerant frequent patterns to generate the candidate fault_tolerant patterns, called FTandidate. The algorithm is based on the data structure of the linear table for fault-tolerant frequent itemset mining. This method does not need recursion, so it reduces the consumption of mining space. At the same time, the paper proposed a deduplication algorithm to remove the support for repeat calculations. So the algorithm has a strong advantage in spatial performance. In addition, the algorithm only needs to mine two horizontal chains of the FTandidate, thus reducing the consumption of mining time. Finally, the paper shows the time performance and space performance of the proposed algorithm under sparse datasets and dense datasets. The results show that our algorithm has better mining time than other algorithms, and the horizontal chain reduces the memory occupation of the algorithm.

关键词： The linear table Fault-tolerance mining algorithm

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A Novel Muilti-layer Associate Rules mining algorithm for Relations between Carbon Emission Futures Price and European Economy 22

A Novel Muilti-layer Associate Rules Mining Algorithm for Re...

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Proceedings of the 2022 6th International Conference on E-Business and Internet

作者： Bing He Wei Xiao Yuanyuan Hu Pingping Xiang State Grid Sichuan Electric Power Company Tianfu New District Power Supply Company China Chengdu University of Technology China

ISBN: (纸本)9781450398640

The multi-dimensional association rule algorithm in data mining was used to quantitatively analyze the influence relationship between carbon emission futures price (CEFP) and ten influencing Europe economic factors in four aspects: population and employment, consumption, domestic product, foreign trade. The results showed that, in terms of population and employment factors, unemployment was negatively correlated with CEFP. In terms of consumption, CEFP is positively correlated with consumption level. In terms of domestic product, CEFP price is positively correlated with gross domestic product, gross added value and total economy. Data mining technology is used to quantitatively analyze the correlation degree between CEFP and influencing factors, in order to provide scientific basis for the relevant departments to invest CEFP.

关键词： Multi-layer associate rules Carbon emission futures price mining algorithm European economy

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Study on microblog public opinion data mining algorithm based on multi-visual clustering model

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2020年第2期13卷 151-165页

作者： Li, Lin-lin Hou, Wei-zhen Liu, Jing Renmin Univ China Sch Informat Resources Management Beijing 100872 Peoples R China Minist Transport Informat Inst Transport Planning & Res Inst Beijing 100028 Peoples R China

Because of the random distribution of microblog public opinion data, it is difficult to mine, this paper proposes a microblog public opinion data mining algorithm based on multi vision clustering model. It constructs the phase space distribution structure model of microblog public opinion data, the fuzzy association rule distribution set of microblog public opinion data, analyses the high-order statistical characteristics of microblog public opinion data, and advances the data of fuzzy clustering center according to the different statistical characteristics Row partition block scheduling. The binary structure of microblog public opinion data is reconstructed in the virtual database, and multi angle fuzzy clustering is carried out according to the reconstruction results to realise the optimised mining of microblog public opinion data. The simulation results show that the mining time of this method is up to 4.13 MS and the mining accuracy is up to 100%.

关键词： multi-visual clustering microblog public opinion data mining algorithm simulation

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Fast association rule mining algorithm for network attack data

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2017年第6-7期20卷 1465-1469页

作者： Song, Junhui Xie, Hua Feng, Yan Xinyang Normal Univ Coll Comp & Informat Technol Xinyang 464000 Peoples R China Xinyang Vocat & Tech Coll Coll Check Technol Xinyang 464000 Peoples R China

Current risk data generated by network attack data is lack of predictability, in this paper, a fast association rule mining algorithm for network attack data is proposed. Based on the related data, the fuzzy theory is used to introduce the frequency of network attack events into the association rules, and based on the genetic algorithm, the concept of interest degree and approximation are introduced to improve the membership function which can establish the network attack data association rules to achieve rapid data mining. The experimental results showed that the proposed algorithm has certain accuracy and efficiency advantages.

关键词： Network attack data Association rules mining algorithm Fuzzy theory

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Research and Application of Association Rule mining algorithm on Teaching and Learning of Distance Education Platform

Research and Application of Association Rule Mining Algorith...

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2018 4th International Conference on Education,Management and Information Technology(ICEMIT 2018)

作者： Shaozhen Huang Hainan Open University

This paper introduces the basic principles of association rule mining algorithms, and in turn studies association rule mining algorithms based on the number of variables（dimensions） involved in mining, the level of abstraction of data, and the categories of processing variables（Boolean and numeric）. This paper summarizes, analyzes and compares some typical algorithms. Finally, the research direction of association rule mining algorithms is prospected.

关键词： association rule mining algorithm distance education platform

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An Improved MFP-Miner mining algorithm and Its Application in Book mining

An Improved MFP-Miner Mining Algorithm and Its Application i...

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2017 4th International Conference on Engineering Technology and Application (ICETA 2017)

作者： Chi Zhang Fang Xiao Huazhong University of Science and Technology Library National Laboratory for Optoelectronics Huazhong University of Science and Technology

With purchase of new books every year, collection of books in a library will continuously increase. As circulation data is updated every day, database of a library will also increase while minimal support degree will change due to different required correlation degree. To satisfy requirements of library readers as soon as possible with book allocation, mining result needs to be updated continually. Therefore, this thesis proposes an IM-Miner algorithm to realize synthesized update and mining of maximal frequent item sets with database and minimal support degree varying at the same time. IM-Miner algorithm makes the most of FP-Tree features and doesn’t need to generate maximal frequent candidate item sets during mining. As generation of maximal frequent item sets only occurs in FP-Tree, no scanning of transactional database is required. Experimental results showed that IM-Miner algorithm is more efficient than other algorithms.

关键词： data mining frequent item mining correlation analysis mining algorithm

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Scheduling constraints and implementation in teaching management systems incorporating association rule mining algorithms

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED DEVELOPMENT 2024年第3期14卷 229-248页

作者： Yan, Jin Li, Meng Ma, Xiaoyan Wang, Junmian Hebi Automot Engn Profess Coll Dept Basic Teaching Hebi 458030 Henan Peoples R China Xian Siyuan Univ Sch Arts & Business Xian 710038 Shaanxi Peoples R China Yanan Univ Sch Phys Educ Yanan 716000 Shaanxi Peoples R China

This paper examines class schedules, precautions, and association rule algorithms and builds a more scientific class scheduling system. Data mining technology association rules handle scheduling conflicts. This method extracts efficient negative sequence rules from patterns. Using local utility value and utility confidence, e-HUNSR formalises the problem of efficient negative sequence rules, generates candidate rules and a pruning strategy quickly, designs a data structure to store the necessary information, and proposes an efficient way to compute the antecedent local utility value and a simplified utility value calculation. Association rules and mining are used to solve the scheduling problem. The system can conduct course queries, OSes, and performs well in data mining. After experimental verification, the hybrid method with different scheduling condition criteria obtains 98.12% course selection satisfaction. Rule satisfaction averages 94.98%, and intelligent scheduling system scheduling efficiency is 91.91%. Adding fresh ideas and methods to the intelligent scheduling system increases instructional resources and university scheduling. Smarter university timetable management allocates teaching resources and completes education and teaching plans.

关键词： association rules data mining scheduling algorithms scheduling constraints and implementation teaching management system incorporating association rule mining algorithm

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Tacit knowledge mining algorithm based on linguistic truth-valued concept lattice 10

Tacit knowledge mining algorithm based on linguistic truth-v...

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10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Yang, Li Wang, Yuhui Xu, Yang North China Univ Water Resources & Elect Power Sch Math & Informat Sci Zhengzhou 450046 Peoples R China Henan Agr Univ Coll Mech & Elect Engn Zhengzhou 450002 Peoples R China Southwest Jiaotong Univ Intelligent Control Dev Ctr Chengdu 610031 Peoples R China

ISBN: (纸本)9781467393232

Natural language processing is one of the important research in the field of artificial intelligence, and tacit knowledge expressed by natural language is the research hotspot. In order to provide a mathematical tool for mining tacit knowledge, we establish a concrete model of 6-ary linguistic truth-valued concept lattice to deal with natural language and introduce a mining algorithm of tacit knowledge through the structure consistency. Specifically, we utilize the attributes to depict knowledge, propose the 6-ary linguistic truth-valued object tacit context and homotype context to characterize tacit knowledge, and research the necessary and sufficient conditions of forming tacit knowledge. We respectively give the generating algorithm of the linguistic truth-valued homotype context and the constructing algorithm the 6-ary linguistic truth-valued concept lattice.

关键词： artificial intelligence concept lattice tacit knowledge linguistic truth-valued concept lattice mining algorithm

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mining High Utility Itemsets Using Prefix Trees and Utility Vectors

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第10期35卷 10224-10236页

作者： Qu, Jun-Feng Fournier-Viger, Philippe Liu, Mengchi Hang, Bo Hu, Chunyang Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Hubei Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518055 Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Key Lab Big Data & Intelligent Educ Guangzhou 510631 Guangdong Peoples R China

High utility itemsets can reveal combinations of items that have a high profit, expense, or importance. mining high utility itemsets in a database with n items generally results in a huge search space, composed of 2(n )itemsets, and heavy utility calculations for the explored itemsets. Previous algorithms using prefix tree structures perform two phases, namely candidate generation and testing. To avoid generating candidate itemsets, one-phase algorithms use list or hyper-link structures and have been proven to be superior to two-phase algorithms. However, it should be noted that a prefix tree is still an efficient structure for itemset mining problems, and especially algorithms using prefix trees such as FP-Growth have shown excellent performance for mining frequent itemsets. This paper proposes Hamm, a High-performance algorithm for mining high utility itemsets. Hamm employs a novel TV (prefix Tree and utility Vector) structure and mines high utility itemsets in one phase without candidate generation. We also develop an efficient optimization which is incorporated into Hamm as a component. Using prefix trees and utility vectors, Hamm outperforms state-of-the-art algorithms on various databases in experiments. Experimental results also show that the proposed optimization remarkably reduces the search space and speeds up Hamm.

关键词： High utility itemset mining algorithm prefix tree utility vector

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A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期192卷 116390-116390页

作者： Du, Xubo Yu, Fusheng Beijing Normal Univ Sch Math Sci Key Lab Math & Complex Syst Minist Educ Beijing 100875 Peoples R China

In real life, there exist a lot of attributed graphs each of which contains attribute information as well as structural information. As time goes on, a group of attributed graphs form an attributed graph sequence. Being the generalization of single-attributed graph sequences, multi-attributed graph sequences are arising vastly and quickly. mining the temporal associations hidden in a multi-attributed graph sequence is in urgent need from data owners. To meet the need and fill the gap of research on mining such kind of temporal associations, we first give a definition of temporal association rules for describing temporal associations in a multi-attributed graph sequence, and then propose a fast algorithm for mining temporal association rules in a multi-attributed graph sequence which is based on the anti-monotonicity of support. The proposed algorithm is designed in two steps, namely finding frequent temporal association rules and verifying the credibility of these rules. Equipped with two novel joining and pruning strategies, the proposed algorithm exhibits much higher efficiency which is specially pursued in the process of rule mining. Experiments performed on synthetic datasets and real datasets show that the proposed algorithm is effective and more efficient than other existing algorithms.

关键词： Temporal association rules Multi -attributed graph sequence mining problem mining algorithm

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