Improved FP-Growth Algorithm Research Articles

Process information optimization method based on process constituent elements model and polychromatic set theory

This research presents a novel model for optimizing process information in manufacturing steps through the utilization of Process Constituent Elements (PCE), with the aim of enhancing the effectiveness of product process information design. To achieve this objective, a systematic analysis is conducted on six dimensions: input, output, resources, value-adding activities, environment, and process control and inspection content. In addition, specific attributes of PCE are investigated, and an improved FP-growth algorithm is employed to extract the optimized structural expressions of typical PCE, thus determining specific expression requirements. The PCE and their attribute relationships are organized into modular mapping rules, resulting in an optimized representation structure based on a polychromatic set approach. The effectiveness of this approach is quantitatively assessed by developing a comprehensive quality indicator evaluation system for process information and using a fuzzy comprehensive evaluation model for analysis.

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.Design/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.FindingsCompared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.Originality/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.HighlightsThe stability of electricity consumption is important to the stable operation of the grid.An improved FP-growth algorithm is employed to explore the influencing factors.The improved algorithm enables the mining of rules containing specific attribute labels.Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Android malware detection based on sensitive patterns

In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry. However, in order to improve the accuracy of detection, the existing static detecting methods sacrifice the excessively high analysis complexity and time cost. Moreover, the correlation between static features leads to redundancy of a large amount of data. Therefore, this paper proposes a static detecting method of Android malware based on sensitive pattern. It uses an improved FP-growth algorithm to mine frequent combinations of sensitive permissions and API calls in malicious apps and benign apps, which avoids the generation of redundant information. In addition, this paper adopts multi-layered gradient boosting decision trees algorithm to train the detection model. And a dual similarity combination method is proposed to measure the similarity between different sensitive patterns. The experimental results show that our proposed detection method has high accuracy and great generalization ability.

Fault diagnosis method of DC transmission system control equipment based on improved FP-growth algorithm

Converter valve control equipment is the core of DC transmission system. It is the key to improve the operation reliability of DC transmission system to conduct defect correlation analysis and fault tracing. In this paper, a fault diagnosis method of DC transmission system control equipment based on improved FP growth algorithm is proposed by studying the correlation of valve based electronic equipment defects. Firstly, based on the basic structure and principle of valve based electronic equipment, the defect data of valve based electronic equipment is collected; Secondly, the original data is preprocessed, and the FP growth algorithm is introduced after quantization and coding; Finally, through the construction of FP Tree, the support and confidence of FP Tree are calculated, and the defect characteristics and influence factors of valve based electronic equipment as well as the correlation between various components are analyzed. The effectiveness of this method is proved by the simulation of the defect data of the electronic equipment of the converter valve base. In practice, it can provide technical support for the operation and maintenance personnel of the converter station to formulate equipment maintenance strategies.

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

The rapid development of computer information technology has made various fault diagnosis and detection technologies emerge in an endless stream. As one of the main transportation vehicles, the detection efficiency of fault diagnosis of railway signal equipment has important practical significance for maintaining the overall safe operation of railways. On the basis of the traditional FP-Growth algorithm, improve the TF-IDF algorithm to realize the weight discretization of text features, and realize the improvement of the FP-Growth algorithm by adjusting the adaptive confidence and support. The FP-Growth algorithm will be improved. FP-Growth algorithm is used for performance tests and applications. The results show that the minimum running time-saving of the proposed algorithm is 1500ms, and the average accuracy of P@N exceeds 85%, which is higher than that of the FP-Growth algorithm (81.4%) and VSM algorithm (82.1%). The PR curve of the improved algorithm is closer to the upper right, which effectively ensures the processing of correlated data, and the overall average precision performance under the influence of positive and negative signal-to-noise ratio values exceeds 95%. And the signal curve generated by the algorithm. The error range of the data under the four fault types of track circuit, turnout, signal, and connecting line floats between 1% and 5%. The improved FP-Growth algorithm can effectively analyze railway fault types and data. Perform analysis and data processing to minimize diagnostic errors.

Variable incremental adaptive learning model based on knowledge graph and its application in online learning system

ABSTRACT With the rapid development of Internet and big data technology, it is common to apply Internet and big data in various fields. Education is also one of the important fields of Internet and big data application, which brings new power to promote China’s education reform and innovation. Nowadays, a large number of online learning systems have been developed and applied in the field of education in China. However, in the face of so many online learning resources, Chinese students often have cognitive loss or cognitive overload. Therefore, the application of adaptive learning model in online learning system provides key to solve these problems. Based on the characteristics of knowledge graph Association, this paper proposes a variable incremental adaptive learning model based on knowledge graph and applies it to online learning system. In this adaptive learning model, this paper studies the construction method and application of knowledge graph. On this basis, in the face of increasing data, this paper uses association rules algorithm to update the knowledge graph. Based on the shortcomings of the existing FP growth algorithm, which still needs to traverse the result set many times, this paper proposes an improved FP growth algorithm. Through the simulation analysis, it is found that the adaptive learning model, constructed by the knowledge graph and the improved FP growth algorithm, can achieve the accurate search of online learning system. And with the increase of students’ search times and time, the content of search will meet their learning needs more and more, complete the knowledge automatic recommendation of online learning system, and avoid the occurrence of network lost and cognitive overload.

Voltage sag severity analysis based on improved FP-Growth algorithm and AHP algorithm

In order to accurately evaluate the impact of voltage sag on both utility and customer, a data mining analysis method based on improved FP-growth algorithm and AHP algorithm is proposed. The proposed method selects and classifies voltage sag characteristics and combines voltage sag severity level to construct a data mining analysis framework. The improved FP-growth algorithm is used for data mining on voltage sag events to improve the efficiency of mining. The association rule matching model is constructed by AHP algorithm for evaluating the voltage sag severity level, which improves the accuracy of the evaluation results. Finally, a practical example verifies the practicability of the proposed method.

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.

The Application of Improved FP-Growth Algorithm in Disease Complications

With the rapid development of computer technology, data mining using association rules has been widely used in all walks of life. FP-Growth algorithm is an association rule algorithm. It does not need to generate candidates, so it has practical value for the study of disease complications. However, it is inefficient and prone to spillover due to its large scale medical data. This paper proposes an improved algorithm based on the FP-Growth algorithm, which sets the minimum support threshold and deletes infrequent item sets to improve the operation efficiency. Experimental results show that this algorithm accelerates the speed of data mining and improves the processing efficiency in the medical data environment.

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy.

Association Analysis of Complex Product Digital Prototype Data Based on the Improved FP-Growth Algorithm

The data of complex product digital prototype with complex relationships have massive, high-dimensional features. Its relevance mining and performance evaluation is a difficult problem. Data mining algorithms can be used to solve the data feature association problem of complex performance. This paper presents an improved FPGrowth algorithm. Improved algorithm adds the weight setting of each dimension attribute to avoid generating redundant false rules due to the uneven distribution of attributes. It constructs the function to map the attribute name and the weighted support count, and reduces the number of times of traversing the frequent item list. It divides the subset of each dimension attribute and constructed the conditional pattern tree of each dimension subset to improve the efficiency of the original algorithm. Based on the improved FP-Growth algorithm, taking the digital prototype data of the bag filter as an example, the parameter combination model was analyzed from the multidimensional angle. It generated the best parameter combination association rule of the bag filter wind performance. It is proved that the improved FP-Growth algorithm is effective and efficient.

Research of Improved FP-Growth Algorithm in Association Rules Mining

Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.

Research of Data Mining Algorithm Based on the Intrusion Prevention System

With the rapid development of network, the security problem of network becomes an issue which has been paid more and more attentions to. Among so many methods of intrusion prevention, data mining is a very effective one. The FP-growth algorithm is the most widely used algorithm for mining frequent item-sets, which is also an algorithm for mining association rules without candidate set. However, the FP-growth algorithm needs large memory when mining large database,and its running speed is slow. In order to overcome these problems, based on the FP-growth algorithm, this paper proposed an optimized algorithm. This paper compared the new algorithm with the previous one based on intrusion prevention model for campus network by experiments. Based on Experiments, we can draw the conclusion that, mining association rules by using the improved FP-growth algorithm can effectively detect the users’ behavior pattern, historical pattern and the current model to calculate the similarity of users, and provides the possibility to accurately judge the user behavior.

DISCOVER MULTI-LABEL CLASSIFICATION USING ASSOCIATION RULE MINING

Association rule mining and classification are two major task of data mining. They are attracted wide attention in both research and application area recently. I propose a method for classification rules from multi-label dataset using association rule analysis. Multi label dataset contains multiple class label attribute for predict target variable. We classify that attribute using different approaches like naviye-baies, decision tree, Back propagation, Neural based classification and association rule based classification. Finding association rule from dataset we have to apply various algorithms like Apriori, Fp-Growth, etc. I proposed Fp-Growth algorithm for finding association rule from dataset because of Fp-Growth is an improved algorithm of Apriori and Fp-Growth is more efficient than Apriori. The number of associations present in even moderate sized databases can be, however, very large - usually too large to be applied directly for classification purposes. Therefore, any classification learner using association rules has to perform three major steps: Mining a set of potentially accurate rules, evaluating and pruning rules, and classifying future instances using the found rule set. Implementation of improved Fp-Growth algorithm gives accurate and classify rule. This approach is more effective, accurate and efficient than other tradition algorithms.

An Algorithm for Mining Frequent Itemsets from Library Big Data

Frequent itemset mining plays an important part in college library data analysis. Because there are a lot of redundant data in library database, the mining process may generate intra-property frequent itemsets, and this hinders its efficiency significantly. To address this issue, we propose an improved FP-Growth algorithm we call RFP-Growth to avoid generating intra-property frequent itemsets, and to further boost its efficiency, implement its MapReduce version with additional prune strategy. The proposed algorithm was tested using both synthetic and real world library data, and the experimental results showed that the proposed algorithm outperformed existing algorithms.

Improved FP-Growth algorithm and its applications in the business association

The FP-Growth algorithm,based on FP-Tree,needs to create a large number of conditional FP-Trees recursively in the process of mining frequent patterns.It is not efficient and not good to apply in mobile communication business cross-selling,in which the association rules mining is business-constraint.Therefore,an items-constraint frequent pattern tree ICFP-Tree and a new ICFP-Mine algorithm which directly mines in the tree were proposed.Theoretical analysis and experimental results show that the ICFP-Mine algorithm is superior to FP-Growth algorithm in memory occupancy and time costs.It has achieved better results in the field of mobile communication business cross-selling applications.

Research and application on Web information retrieval based on improved FP-growth algorithm

A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.