scholarly journals Collaborative filtering recommendation model based on fuzzy clustering algorithm

2018 ◽  
Author(s):  
Ye Yang ◽  
Yunhua Zhang
Keyword(s):  
Collaborative Filtering ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Model Based ◽  
Fuzzy Clustering Algorithm

Research and implementation of e-commerce intelligent recommendation system based on fuzzy clustering algorithm

2021 ◽  
pp. 1-10
Author(s):  
Jinjuan Hu ◽  
Chao Xie
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Rapid Development ◽  
Filtering Algorithm ◽  
Fuzzy Clustering Algorithm ◽  
Analysis Experiment ◽  
Collaborative Filtering Algorithm

After entering the 21st century, the electronic commerce system has affected all aspects of our lives. Whether we read news on our mobile phones or computers or purchase items on our online websites, it greatly facilitates our lives. With the rapid development of short videos, many people like to watch small videos that interest them. The rapid development of e-commerce has facilitated our lives, so that we no longer have to go to many shopping malls to buy our favorite items, and we also no need to change TV stations one by one after watching a program to find our favorite programs. However, due to the rapid development of electronic commerce, there has been a lot of information overload. When users browse the website, items they are not interested in will appear, and even information about online fraud appears. How to filter this information and how to intelligently recommend to users more favorite items is the main research direction of this article. The research of this article is mainly divided into four parts. The first part analyzes the current situation of intelligent recommendation technology research and puts forward the idea of this article. The second part introduces the commonly used collaborative filtering algorithm and the principle and process of the fuzzy clustering algorithm used in this experiment, analyzes the shortcomings of the traditional collaborative filtering algorithm and illustrates the adaptability of the fuzzy clustering algorithm in practical applications. The third part introduces an intelligent recommendation system based on fuzzy clustering, which comprehensively analyzes the characteristics of users and products, makes full use of users’ evaluation information of products, and realizes intelligent recommendations based on content and collaborative filtering. At the end of the article, the comparative analysis experiment with the intelligent recommendation system of collaborative recommendation algorithm further proves the superiority of the intelligent recommendation system of electronic commerce based on fuzzy clustering algorithm in this paper and improves the accuracy of intelligent recommendation.

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections

2007 ◽  
Vol 27 (3) ◽  
pp. 237-248 ◽  
Author(s):  
Xiao-Ying Wang ◽  
Jonathan M. Garibaldi ◽  
Benjamin Bird ◽  
Michael W. George
Keyword(s):  
Lymph Node ◽  
Fuzzy Clustering ◽  
Axillary Lymph Node ◽  
Clustering Algorithm ◽  
Axillary Lymph ◽  
Tissue Sections ◽  
Fuzzy Clustering Algorithm
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