A fuzzy clustering algorithm based on hybrid surrogate model

Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c-means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships.

Service Partition Method Based on Particle Swarm Fuzzy Clustering

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

Ming-style chair parts and its group technology application research

In order to apply the group technology to the production of Ming style furniture, enhance the ability of enterprises to cope with the production of small-batch, multi-variety furniture, this article will take Ming-style chair furniture as an example to make a reasonable division of its parts. The types of Ming-style chair parts are combed, the characteristic information of each component is extracted and the joint mode of mortise and tenon between two parts is summarized. In this way, the process of each mortise and tenon structure is summarized, and the correspondence between mortise and tenon structure and the machinery is established. According to this method, the Ming-style chair parts are summarized and classified into 211 kinds. Based on the similarity of parts processing machinery, all parts of the Ming-style chairs are classified into groups by fuzzy clustering algorithm and MATLAB software calculation. The purpose of this article is to provide a divisional idea for the pre-part classification preparation of Ming-style chair parts in groups for the part family.

Design of an Integrated System of Psychological Health Education Resources for Vocational Students Using Fuzzy Clustering Algorithm Analysis

Mental health is an important part of the growth of students. Many mental health monitoring systems have many problems. When the resource capacity is large, traditional systems have problems such as poor stability, more resource fragments, and untimely system response. For this reason, this paper designs a system of mental health education resource integration for higher vocational students based on a fuzzy clustering algorithm. It builds the overall system architecture based on the SOA service architecture and designs each functional module of the system so that the system has the functions of teaching resource submission, resource audit management, user service, and resource storage. The density function method is used to initialize the initial resource clustering center to obtain the data clustering objective function, and the fuzzy clustering algorithm is used to establish the task model to realize the integration of the mental health education resources of the vocational students. System verification results show that the system has strong stability, fewer node resource fragments, and higher resource integration efficiency.