The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubes

Fadishei, Hamid; Soltani, Azadeh

doi:10.1007/s11227-019-03053-8

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of mining large data frequent itemsets, there is an a priori property that can be used to compress the search space. A priori property belongs to all nonempty subsets of frequent itemsets [10,11]. Combined with this definition, if the itemset X is not frequent, it means that the itemset does not meet the requirements of minimum support.…”

Section: Big Data Frequent Itemsetmentioning

confidence: 99%

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Wang

2021

Security and Communication Networks

View full text Add to dashboard Cite

In order to solve the problem of large data classification of human resources, a new parallel classification algorithm of large data of human resources based on the Spark platform is proposed in this study. According to the spark platform, it can complete the update and distance calculation of the human resource big data clustering center and design the big data clustering process. Based on this, the K-means clustering method is introduced to mine frequent itemsets of large data and optimize the aggregation degree of similar large data. A fuzzy genetic algorithm is used to identify the balance of big data. This study adopts the selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resource, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource. The experimental results show that the proposed algorithm has a low time cost, good classification effect, and ideal parallel classification rule complexity.

show abstract

Section: Big Data Frequent Itemsetmentioning

confidence: 99%

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Wang

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

Forecasting model of electricity market prosperity index based on multidimensional big data

Liu

Yan

et al. 2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The establishment of electricity market prosperity index prediction model is of great significance to the planning, construction, production and decision-making of electricity market. On this basis, from the perspective of industry and industry, this paper puts forward the design of multi-dimensional big data forecasting model of power industry electricity market prosperity index. This paper analyzes the compilation process of the prosperity index of the power industry, studies the pre-processing method of the power data based on the multidimensional big data. Based on the index division algorithm of multi-dimensional big data time difference analysis and the method of compiling the prosperity index of the power industry, the prosperity index of the power market of the power industry is predicted. Through the calculation of the diffusion index method and composite index of power consumption in power industry. Combined with the multi-dimensional big data nonlinear analysis method, it is applied to the prosperity index analysis of the power industry. Finally, the accurate prediction of the electricity market prosperity index of the power industry is realized. Finally, through the simulation test, according to the power data provided by the electric power bureau of a city, the power prosperity index is compiled, and the forecasting effect of the power industry electricity market prosperity index is tested. Experimental results show that the model has high effectiveness in practical application, and can fully meet the research requirements.

show abstract

Massive Power Grid Operational Status Data Analysis System for 3D Visualization

Pan¹,

Hu²,

Zheng³

2021

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)

View full text Add to dashboard Cite

The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubes

Cited by 3 publications

References 21 publications

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Forecasting model of electricity market prosperity index based on multidimensional big data

Massive Power Grid Operational Status Data Analysis System for 3D Visualization

Contact Info

Product

Resources

About