Analysis of Web Log Data Mining Based on Improved Fuzzy Clustering Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1896-1901 ◽  
Author(s):  
Chuan Qi Chen
Keyword(s):  
Data Mining ◽  
Best Practices ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Pattern Mining ◽  
Log Data ◽  
Web Log ◽  
Fuzzy Clustering Analysis ◽  
Fuzzy Clustering Algorithm

Fuzzy clustering analysis is a clustering algorithm based on function best practices, technology and optimal cost function using calculus. Fuzzy clustering, each sample is no longer belong to a class, but belong to a certain degree of membership of each class. In this paper, Web log sequential pattern mining knowledge gained, and visitors have the same browsing mode access to cutting the interaction of users with the Web information space. The paper presents analysis of Web log data mining based on improved fuzzy clustering algorithm. The experiment demonstrates the improved algorithm has better scalability.

Analysis of Fuzzy Clustering for the Adoption in Data Mining

2014 ◽  
Vol 989-994 ◽  
pp. 2047-2050
Author(s):  
Ying Jie Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mathematical Method ◽  
Big Data ◽  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Fuzzy Clustering Methods

Data mining is the general methodology for retrieving useful information from big data. Clustering analysis is a mathematical method of classification for unsupervised machine learning. It can be adopted for data classification in Data mining. This paper combines the clustering process by fuzzy way and then deduces a special clustering algorithm with fast fuzzy c-means (FFCM) method. In summary, the paper illustrates the adoption of a series of fuzzy clustering methods in Data Mining. These methods have improved the computational efficiency with learning as the convergence speed is fast. The methodology of this paper presents significantly meaningful for information retrieval of big data.

Fuzzy Clustering Algorithm of Early Fire Based on Process Characteristic

2010 ◽  
Vol 437 ◽  
pp. 339-343 ◽  
Author(s):  
Ren Cheng Zhang ◽  
Jian Hua Du
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Fire Detection ◽  
Threshold Value ◽  
Fuzzy Clustering Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Process Signature ◽  
Process Characteristic ◽  
Detection Technologies

Traditional fire detection technologies usually measure the smoke particles or the temperature increase resulted from fire. However in the early stage of fire, few particles and low heat are generated. Current fire algorithms is based on comparing the fire variables with a given threshold value, the transient sampled values are often affected by some stochastic disturbances. Consequently current methods are hardly alarm fire fleetly and reliably and often give false or failing alarm. A new fire detecting technology was presented based on early fire process signature and fuzzy clustering algorithm. The process eigenvector is made up of CO concentration in detected environment as well as its increasing rate and acceleration. The eigenvectors are divided into two categories of real fire and non-fire, the two cluster centers are obtained by using fuzzy clustering analysis. According to threshold membership principle, the real fire sources can be distinguished from non-fire sources successfully. The result of experiments has shown that the presented technology is feasible for early fire detecting with lower rate of false and failing alarm, and give fire alarm much early than any other traditional method.

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.

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