Data mining is a technique dedicated to data analysis and understanding and to reveal the knowledge contained in data. It has become one of the important goals of the application of information technology in the future. Association rule mining is a fruitful and active branch of data mining, and it is a further research topic for researchers. Faced with large databases, association rules mining needs to be improved in terms of mining efficiency, availability, accuracy, and so on. Therefore, this paper studies the association rule mining algorithm based on large data. The paper introduces the knowledge of large data and the basic knowledge of data mining and the mining theory of association rules, and focuses on the association rules mining algorithm, including the Apriori algorithm and the FP-tree algorithm. In a word, on the basis of analyzing and classifying the existing data mining research results and the prototype system, the paper studies the theory model and algorithm of association rule data mining.

30.Research on Association Rules Mining Algorithm Based on Large Data

Power plant performance can decrease along with its life span, and move away from the design and commissioning targets. Maintenance issues, operational practices, market restrictions, and financial objectives may lead to that behavior, and the knowledge of appropriate actions could support the system to retake its original operational performance. This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions. The selected operational variables are evaluated in respect to their impact on the system performance, quantified by the Variable Importance Index. That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation, and should be controlled with more attention. Principal Component Analysis (PCA) and k-means++ clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant. The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones, to finally group recommended settings to achieve the target conditions. Results show performance gains in respect to the average historical values of 73.5% and the lowest efficiency condition records of 68%, to the target steam generator efficiency of 76%.

Increasing power plant efficiency with clustering methods and Variable Importance Index assessment

In this paper, a case study on the role of fuzzy logic (FL) in data mining and machine learning is carried out. It is outlined that, in order to draw more attention of data-mining and machine-learning communities to FL, studies on FL could be more focused not on the activities that fuzzy methods can perform better but rather on the activities that fuzzy methods can perform and the non-fuzzy ones can’t. Such approach takes us away from discussing quantitative differences between fuzzy and non-fuzzy methods to discussing qualitative differences, which are possibly more favorable objects of scientific curiosity. Following the outlined suggestion, a novel speed-up technique is proposed in this paper to support association rule mining (ARM). The proposed technique is a clustering-based one and provides fusion of clustering and ARM. The catchy feature of this technique is that it works well if applied in fuzzy ARM and doesn’t work well if applied in non-fuzzy ARM. The proposed technique is put through experimental verification involving several real-world datasets, and the results substantiate its effectiveness.

Value of Fuzzy Logic for Data Mining and Machine Learning: A Case Study

In order to solve the problem of mining quantitative association rules, an algorithm named Fuzzy Pattern Fusion based on Competitive Agglomeration (FPF-CA) is developed in this paper. The proposed algorithm is based on the superior functionalities of Fuzzy Pattern Fusion (FPF) for mining quantitative association rules and Competitive Agglomeration (CA) for finding the optimal number of clusters. The popular data set of UCI machine learning repository is used to demonstrate the feasibility of the FPF-CA algorithm. The simulation experiment results show that the proposed algorithm can efficiently mine quantitative association rules according to the actual data distribution.

Fuzzy Clustering-Based Quantitative Association Rules Mining in Multidimensional Data Set

In order to solve the coal-fired boiler operation optimization problem with multiple main controllable parameters, a fuzzy pattern fusion based on competitive agglomeration (FPF-CA) algorithm is developed and applied in this paper. A simplified mathematical model for coal-fired boiler systems is applied in terms of both historical operational and thermal efficiency data under different load conditions. The FPF-CA algorithm can be applied to perform information fusion in terms of combining fuzzy clusters of some quantitative attributes with generated quantitative association rules. The historical data collected from a 130/2.82-M circulating fluidized bed (CFB) boiler being installed in a production scale thermal power plant is used to specifically analyze, and the simulation experiment results show the application of FPF-CA algorithm is a decision oriented intelligent technology and may provide an efficient results for coal-fired boiler operation optimization in thermal power plant.

The Application of Fuzzy Pattern Fusion Based on Competitive Agglomeration in Coal-Fired Boiler Operation Optimization

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Fuzzy Clustering-Based Quantitative Association Rules Mining in Multidimensional Data Set

The Application of Fuzzy Pattern Fusion Based on Competitive Agglomeration in Coal-Fired Boiler Operation Optimization