An improved association rule mining algorithm for large data

Zhao, Zhenyi; Jian, Zhou; Gaba, Gurjot Singh; Alroobaea, Roobaea; Masud, Mehedi; Rubaiee, Saeed

doi:10.1515/jisys-2020-0121

“…Database methods mainly include attribute-oriented induction methods and multidimensional database analysis methods [5]. Aiming at the current situation that a large amount of data in the power system cannot be effectively used, Zhao et al proposed a data warehouse-based method for sorting, extracting, purifying, and transforming existing data, which can provide fast and efficient decision support systems and solutions for efficient data response [6]. Chen et al compared the performance of data mining technology using fuzzy inference system and traditional methods in short-term load forecasting; the analysis shows that the data mining technology using the fuzzy inference system can better conform to the actual situation of power production in the prediction [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Substation Operation Information Maintenance Based on Intelligent Data Mining

Zhang

¹

,

Yuan

²

2022

Wireless Communications and Mobile Computing

1

0

1

View full text Add to dashboard Cite

In order to solve the problems of redundancy of substation operation information, diversification of monitoring system versions, and heterogeneous data, the author proposes a remote-based intelligent data mining system for substation automation. Use data mining technology to classify and standardize the original alarm data, and associate it with the equipment ledger system to obtain a data warehouse of equipment parameters, status information, and related historical fault information. On this basis, the improved Apriori algorithm is used to extract strong association rules that satisfy the minimum trust threshold from the data warehouse, which provides decision-making basis for the operation and maintenance management of substation equipment. Experimental results show that using the improved Apriori algorithm to perform data mining on the substation operation alarm information in 143 intervals of a power supply bureau substation, the following strong association rules are obtained: association rule 1, “handcart test position ∩ device running alarm ∩ alarm signal has been reset interval debugging and maintenance”, γ support = 57 % , γ confidence = 69 % , and association rule 2, “Protection action∩ switch sectional position ∩ reclosing action ∩ total accident (hold) ∩ switch sectional signal has been reset ∩ reclosing action signal has been reset ∩ total accident (hold) signal has been reset Temporary equipment failure”, γ support = 41 % , γ confidence = 79 % . The effectiveness of the proposed data mining method is verified by a field case.

show abstract

“…Zhao et al [21] first proposed in their analysis of the market basket problem to discover customer buying patterns in merchandising, which can be used to guide merchants in the scientific arrangement of stocking, inventory, and shelf design [21]. Yang [22] proposed algorithm that uses a division-based technique to partition the database processing effectively reducing the number of database scans during the mining process and reducing the burden [22].…”

Section: Association Rule Algorithmmentioning

confidence: 99%

Integration and Recommendation of Multimedia Network-Assisted English Instructional Resources Based on Association Rules Mining

Liu¹

2022

Mobile Information Systems

2

0

1

View full text Add to dashboard Cite

The integration of multimedia teaching refers to a novel teaching mode that combines multimedia, information resources, and information methods with content and teaching process in the teaching process with the guidance of contemporary educational ideas and organically unifies them on a spatial and temporal platform, so as to better accomplish the teaching tasks. However, with the deepening of network teaching and school connotation construction, various resources such as media materials, courseware, network courses, and teaching cases generated by education and teaching, as well as academic works, papers, patents, project results, and lectures of experts and professors gathered by scientific research, are increasing. In this situation, how to integrate teaching resources and courses to provide students with an independent learning platform and an information-based learning environment has become an urgent issue. For this problem, data mining has shown a strong vitality, and data mining can find out the potential connection between datasets. Association rule mining is the most researched and widely used data mining method. In this paper, we propose a multimedia network-assisted English integration of educational resources method based on association rule algorithm to give full play to the role of “multimedia technology” and realize the integration of traditional English teaching and information network culture, so as to achieve the purpose of optimizing English teaching. The experimental results show that with the increase of data volume, the average testing time of Apriori is reduced by 56.49 s compared with MapReduce, so the advantage of Apriori algorithm is more obvious. Therefore, this research can effectively provide students with rich and colorful personalized resources, realize high-performance interactive learning, and help students cultivate their independent learning ability as well as their lifelong learning habits.

show abstract

“…When choosing a cluster center, initially acquire the value point (mean), denoted by c l , of the data in the class [16,17]. In the next stage, calculate the distance between the mean value point and other data, and obtain the adjacent objects of c l while agreeing to the distance index R. In the third stage, compute the value point (mean), denoted by c , l , of the adjacent objects.…”

Section: A Dynamic Algorithm For Determining Cluster Centersmentioning

confidence: 99%

Application of the R-Tree Clustering Model in Medical Information Retrieval

Dai

¹

,

Líu

²

2022

Mobile Information Systems

0

View full text Add to dashboard Cite

Hospitals produce a large amount of medical information every day. In the face of medical big data, the existing data processing methods cannot meet expectations and need to be continuously optimized. In the database system, when the stored objects are very large, and then the efficiency of data retrieval is a major bottleneck, therefore restricting the application of medical information. For that reason and to improve the efficiency of information retrieval, it is necessary to add an index to the information object and filter the dataset participating in the connection retrieval through the index. In this paper, an information retrieval technique grounded on the R-tree clustering model index is proposed for massive hospital information. The R-tree clustering model is constructed in massive hospital information by using the dynamic determination clustering center (DCC) algorithm. Finally, the superiority of the method is proved by simulations. The experiments and empirical evaluation show that the proposed R-tree clustering model index significantly improves data retrieval efficiency.

show abstract

An improved association rule mining algorithm for large data

Cited by 38 publications

References 37 publications

Substation Operation Information Maintenance Based on Intelligent Data Mining

Substation Operation Information Maintenance Based on Intelligent Data Mining

Integration and Recommendation of Multimedia Network-Assisted English Instructional Resources Based on Association Rules Mining

Application of the R-Tree Clustering Model in Medical Information Retrieval

Contact Info

Product

Resources

About