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Therefore, collaborative fuzzy clustering is a knowledge-based clustering which aims to improve the local clusterings of the data stored in multiple data sites by observing data sharing restrictions and using the knowledge gained from the structure of other data sites.
Collaborative Learning Grouping Based On Fuzzy Clustering.
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In the light of the idea of collaborative learning, a collaborative optimization of clustering by fuzzy c-means and weight determination by ReliefF (Co-WFCM) is ...
This paper discusses a research on CSGF to form homogeneous groups using a Fuzzy C-Means Clustering method. The parameter used is learning styles according to ...
In this study, we introduce a new clustering architecture in which several subsets of patterns can be processed together with an objective of finding a ...
Dan Yu, Xinmeng Chen, Jinshuo Liu: Collaborative Learning Grouping Based On Fuzzy Clustering. IC-AI 2007: 78-82. manage site settings.
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A strategy was designed to combine the K-means clustering algorithm and the learning styles of different students to provide a valuable reference for student ...
Abstract— One of the issues in collaborative learning is forming groups based on criteria that have been determined before such as grades, learning style, ...
Revealing the common underlying structure of data spread across multiple data sites by applying clustering techniques is the aim of collaborative clustering ...
A Fuzzy Clustering Based Collaborative Filtering Algorithm for ...
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It is a technique to make location recommendation based on a group of users with common check-in behaviours. Ye proposed a user collaborative filtering method ...
Abstract. In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields.