A Clustering Approach for Collaborative Filtering Under the Belief Function Framework

2017 ◽  
pp. 169-178 ◽  
Author(s):  
Raoua Abdelkhalek ◽  
Imen Boukhris ◽  
Zied Elouedi
Keyword(s):  
Collaborative Filtering ◽  
Belief Function ◽  
Clustering Approach

A dynamic collaborative filtering system via a weighted clustering approach

2016 ◽  
Vol 175 ◽  
pp. 206-215 ◽  
Author(s):  
Aghiles Salah ◽  
Nicoleta Rogovschi ◽  
Mohamed Nadif
Keyword(s):  
Collaborative Filtering ◽  
Weighted Clustering ◽  
Clustering Approach

INNER AND OUTER APPROXIMATION OF BELIEF STRUCTURES USING A HIERARCHICAL CLUSTERING APPROACH

2001 ◽  
Vol 09 (04) ◽  
pp. 437-460 ◽  
Author(s):  
THIERRY DENŒUX
Keyword(s):  
Hierarchical Clustering ◽  
Numerical Experiments ◽  
Belief Function ◽  
Outer Approximation ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Belief Structures ◽  
Outer Approximations ◽  
Clustering Approach

A hierarchical clustering approach is proposed for reducing the number of focal elements in a crisp or fuzzy belief function, yielding strong inner and outer approximations. At each step of the proposed algorithm, two focal elements are merged, and the mass is transfered to their intersection or their union. The resulting approximations allow the calculation of lower and upper bounds on the belief and plausibility degrees induced by the conjunctive or disjunctive sum of any number of belief structures. Numerical experiments demonstrate the effectiveness of this approach.

scholarly journals Enhanced K-Means Clustering Algorithm Using Collaborative Filtering Approach

2017 ◽  
Vol 10 (2) ◽  
pp. 474-479
Author(s):  
Ankush Saklecha ◽  
Jagdish Raikwal
Keyword(s):  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Clustering Approach ◽  
Improving Accuracy ◽  
Accuracy Performance ◽  
Number Of Iterations ◽  
Filtering Approach ◽  
Selection Of

Clustering is well-known unsupervised learning method. In clustering a set of essentials is separated into uniform groups.K-means is one of the most popular partition based clustering algorithms in the area of research. But in the original K-means the quality of the resulting clusters mostly depends on the selection of initial centroids, so number of iterations is increase and take more time because of that it is computationally expensive. There are so many methods have been proposed for improving accuracy, performance and efficiency of the k-means clustering algorithm. This paper proposed enhanced K-Means Clustering approach in addition to Collaborative filtering approach to recommend quality content to its users. This research would help those users who have to scroll through pages of results to find important content.

Sign in / Sign up

Export Citation Format

Share Document