scholarly journals Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

2018 ◽  
Vol 29 (1) ◽  
pp. 583-595 ◽  
Author(s):  
V. Raju ◽  
N. Srinivasan
Keyword(s):  
Clustering Algorithm ◽  
Recommendation System ◽  
Model Data ◽  
Web Page ◽  
Third Phase ◽  
Consumer Access ◽  
User Data ◽  
Fuzzy C Means Clustering ◽  
Neural Network Algorithm ◽  
Projected Process

Abstract This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

Improved Web page clustering algorithm based on partial tag tree matching

2010 ◽  
Vol 30 (3) ◽  
pp. 818-820
Author(s):  
Rui LI ◽  
Jun-yu ZENG ◽  
Si-wang ZHOU
Keyword(s):  
Clustering Algorithm ◽  
Web Page ◽  
Web Page Clustering

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

Semi-Automatic Online Tagging with K-Medoid Clustering

2014 ◽  
Vol 24 (08) ◽  
pp. 1115-1130 ◽  
Author(s):  
He Hu ◽  
Xiaoyong Du
Keyword(s):  
Clustering Algorithm ◽  
Prototype System ◽  
Web Pages ◽  
Web Page ◽  
Web Browser ◽  
User Input ◽  
Browser Extension ◽  
Annotation Process ◽  
Efficiency And Effectiveness ◽  
Automatic Mechanism

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios.

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