Open AccessResearch On Data Mining Algorithm In Power Marketing Analysis
Author(s) -
Hao Miao Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1635/1/012002
Subject(s) - association rule learning , data mining , computer science , data set , big data , algorithm , data stream mining , set (abstract data type) , artificial intelligence , programming language
In the analysis of power market economic evaluation indicators, it is more common to use technical data mining for data calculation based on technology maturity analysis. This paper is based on a distributed computing platform for data mining, and parallelizes the traditional FP-Growth algorithm, so that the FP-Growth algorithm can be applied to the mining of big data association rules. Experimental results show that the algorithm can greatly improve the efficiency of mining association rules for massive data. The model is trained and tested on a distributed cluster built into the laboratory using customer electricity data, and then the predictions are validated to determine whether the difference between the predicted values and historical data for the same period exceeded the set threshold.