Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis

Lin, Kuo-Ping; Lin, Ching-Lin; Hung, Kuo-Chen; Lu, Y.; Pai, Ping-Feng

doi:10.1109/ieem.2012.6838017

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…In order to choose suitable learning method, clustering was addressed [3]. Lin et al proposed the kernel intuitionistic fuzzy -means clustering (KIFCM) and applied it in elearning customer analysis [4]. Another clustering approach applied in detecting learners' behavioral patterns to support individual and group-based collaborative learning was put forward by Köck and Paramythis [5].…”

Section: Introductionmentioning

confidence: 99%

A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

Niu

Niu²,

Su³

et al. 2015

Mathematical Problems in Engineering

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In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%