FP-Growth Association Rules Research Articles

English Teaching Project Quality Evaluation Based on Deep Decision-Making and Rule Association Analysis

A large amount of course data has been accumulated in the long-term teaching activities of universities. It is of great research value to use the data resources to analyze the course teaching status and provide decision support for improving the course teaching quality. In this paper, we design and implement a course evaluation system based on association rules and cluster analysis, analyze the functional requirements of the course evaluation system, and pre-process the course evaluation data. Students’ performance data are analyzed by FP-growth association rules, and then clustered by K-means, which can improve the accuracy of data evaluation.The evaluation index system of university English teaching quality under the concept of “Thinking and Government” is established. With the results of the sample survey, the main problems of the evaluation method are summarized and analyzed, and corresponding suggestions are put forward, which provide an important reference for promoting the reform of college English course.

Practice and Exploration of Music Solfeggio Teaching Based on Data Mining Technology.

People can learn, understand, and use music using the solfeggio method. People involved in professional music training have given it importance because it is a fundamental discipline of enlightenment music and a necessary training method and technical theory course to enter the professional level. This article presents an in-depth analysis of the music curriculum model based on data mining technology. Additionally, a music style classification method based on FP Growth association rules mining is developed, which decreases the number of frequent element sets needed and speeds up database scanning. To better match the characteristics of music, a multirepetition structure model is used, and the MFCC (Mel-frequency cepstral coefficients) function is used to set the repetition structure model. The results demonstrate that the music style classification method put forth in this article has clear advantages in terms of efficiency: when the minimum support is 0.2 percent, this method's execution time is about 33 percent compared to the other methods. The outcomes of the experiment suggest that this method can more accurately capture the repeatability of music and enhance separation performance to some extent.

Quality Deviation Control for Aircraft Using Digital Twin

Abstract During the manufacturing process of aircraft, quality deviation problems inevitably occur due to the high complexity of aircraft design, manufacturing errors, tooling mistakes, human factors, environmental influences, design defects, and other factors. The current quality deviation control system of civil aircraft suffers from two problems: (1) quality deviation control data are scattered in more than 100 management systems, and it is difficult to extract quality data-related information from the whole life cycle of the aircraft involving the main manufacturer and each supplier and (2) there is a lack of quality data analysis and a closed-loop information-physics fusion system for quality deviation control. Thus, it is difficult to locate the quality deviation problems and it takes a long time to deal with these problems as well. In this paper, a digital twin-based quality deviation control model is proposed. Through the digital twin modeling based on asset management shell technology, the multi-source and heterogeneous quality deviation data can be extracted and integrated. Furthermore, to deal with the second problem, a quality deviation system has been built based on digital twin. In this system, the aircraft quality deviation data can be analyzed by the FP-growth association rule algorithm and the results are provided through the system to guide the assembly site, improving the efficiency and accuracy of quality problem-solving in the physical world. In addition, a case study is stated, where the proposed approach is applied to deal with the aircraft quality deviation problems.

RESEARCH ON STUDENT BEHAVIOR INFERENCE METHOD BASED ON FP-GROWTH ALGORITHM

Abstract. How to use modern information technology to efficiently and quickly obtain the personalized recommendation information required by students, and to provide high-quality intelligent services for schools, parents and students has become one of the hot issues in college research. This paper uses FP-growth association rule mining algorithm to infer student behavior and then use the collaborative filtering recommendation method to push information according to the inference result, and then push real-time and effective personalized information for students. The experimental results show that an improved FP-growth algorithm is proposed based on the classification of students. The algorithm combines the student behavior inference method of FP-growth algorithm with the collaborative filtering hybrid recommendation method, which not only solves the FP-tree tree branch. Excessive and collaborative filtering recommendation algorithm data sparseness problem, can also analyze different students' behaviors and activities, and accurately push real-time, accurate and effective personalized information for students, to promote smart campus and information intelligence The development provides better service.

Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets

Data mining is a study that uses statistical knowledge, mathematical calculations, artificial intelligence methods, machine learning by extracting and identifying useful information and related knowledge from various large databases.. One of them is looking for itemsets combination from the data stack, the search process can be done using the Apriori Association Rules algorithm, the FPGrowth Association Rule and Closed Association rule. The three algorithms are some of the implementations of frequent itemsets search methods. Two datasets will be tested, namely retail datasets and accidents datasets derived from fimi datasets, with the aim of knowing the behavior of the three algorithms against the dataset model we tested. Each algorithm will test both datasets, with minimum support values for retail datasets ranging from 0.01-0.05 and min-conf 0.01-0.05. Likewise on the accident min-sup and min-conf 0.6-1 datasets. For retail algorithm data with the fastest processing time, the FP-Growth AR algorithm is more than 85% compared to Apriori-AR, followed by AR-Apriori and Apriori-Close-AR. For detailed memory usage results the Apriori-AR algorithm is lighter and outperforms. Accident data the experimental results show that FP-Growth-AR is a little more memory efficient and the fastest process is Apriori-Close, while the Apriori-AR with the longest processing time.

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.

An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth

In order to recommend an efficient drawing inspecting expert combination, an expert combination is selected by an expert recommendation algorithm based on Pearson’s correlation coefficient and FP-growth. By introducing the Pearson correlation coefficient and the FP-growth association rule algorithm, the expert recommendation algorithm can accurately select the participating experts in the historical project similar to the scale of the project to be reviewed, and combine the experts to calculate and obtain the expert group with the highest fit, namely, the expert combination of project to be reviewed. This expert recommendation algorithm based on Pearson correlation coefficient and FP-growth can effectively recommend a kind of expert group with the highest efficiency of collaborative review, which solves the problem of how to recommend efficient expert combination accurately for drawing inspecting system.

An improvement of FP-Growth association rule mining algorithm based on adjacency table

FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.

FEATURE SELECTION USING ASSOCIATION RULES FOR CBIR AND COMPUTER AIDED MEDICAL DIAGNOSTIC

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized tomography has resulted in a sudden increase in the number and significance of medical images. Content Based Image Retrieval techniques are being extensively used to aid diagnosis by comparing with similar past cases and improvising Computer Aided Diagnosis. In this paper, it is proposed to extract features in the frequency domain using Walsh Hadamard transform and use FP-Growth association rule mining to extract features based on confidence. The extracted features are classified using Naïve Bayes and CART algorithms and the proposed method’s classification accuracy is evaluated. Experimental results show that classification accuracy for Naïve Bayes is 100 and 96.8 for CART on application of proposed method.

Comprehensive Energy Efficiency Analysis of Buildings Based on FP-Growth Association Rules

Energy efficiency analysis is an important basis for the establishment of a sound scientific energy saving mode and optimization operation scheme. Data mining technology is suitable to be applied in the comprehensive energy efficiency analysis because of its capability of processing large volume of data, eliminating redundant information, looking for hidden information and other unique advantages. This paper presents a comprehensive energy efficiency analysis technique based on frequent pattern growth (FP-Growth) association rules for buildings. The technique can effectively analyze the association relationship among the sub-metering data and provide support for the further development of energy saving programs. The proposed method was used in the energy efficiency analysis program of to a commercial building in Shanghai. The results prove the effectiveness and practicality of the proposed technique. 

A Refined Continuous Ant Colony Optimization Based FP-Growth Association Rule Technique on Type 2 Diabetes

In recent years, Diabetes mellitus almost tops the list of chronic diseases worldwide among the major public health challenges. Diagnosing diabetes at the preliminary stage is undoubtedly challenging as it involves varying complexities and inter relation and dependence on several factors that affect it directly or indirectly. Due to large number of diabetic patients in recent years, desperate measures have to be devised and developed to facilitate medical diagnostic decision support systems that help doctors, researchers and medical practitioners during and after the process of diagnosing Diabetes. In this paper, the Association Rule Mining and Enhanced FP-Growth Algorithm has been used as a reference to propose a new algorithm that is basically has similar functionalities as that of the Ant Colony Optimization algorithm and one that improvises association rule mining results. The Refined Continuous Ant Colony Optimization or CACO deploys a meta-heuristic approach and has been devised and enthused by actual ant colonies behavior along with the sustained continuous domains. Preliminarily association rules so produced by the Enhanced FP-Growth algorithm are deployed thereafter which rules from weakest set are found on the basis of threshold value and then further used by the Ant Colony algorithm so that association rules are reduced and a better quality of rules are discovered as a result of the efforts. The study as well the research presented here aims at reducing database scanning by optimizing as well by improving the quality of rules that are produced for CACO.

Research and Application of Information Operation and Maintenance Fault Diagnosis Model for Power Grid Enterprise

Power industry informational has become the general trend in new era, for power dispatching operation and mainte-nance center, how to ensure that normal operation of various software and hardware resources is a important yet unsolved problem. The paper makes full use of operation and maintence supervision method, presents a information operation and maintenance fault diagnosis model based on FP-Growth association rules algorithm for power grid enterprise. Firstly, information system operation and maintenance supervision model of power grid is described, according to power enterprise regulations and practical experience of operation and maintenance personnel, it makes a series of operation and maintenance indicators. Then above this foundation detailing the fault diagnosis model is constructed, analyzing diagnosis reasoning process under the different the case of non-compliance indicators, and applying it to the actual operation project that the power grid enterprise information system real-time monitoring plat-form (ISRMP). Finally, preliminary application of ISRMP in Information and Communication Company of Jilin Electric Power.