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

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ke Niu ◽  
Zhendong Niu ◽  
Yan Su ◽  
Can Wang ◽  
Hao Lu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Learning Systems ◽  
Mixed Data ◽  
Continuous Data ◽  
Significant Information ◽  
Web Based ◽  
Study Guides ◽  
Web Based Learning ◽  
User Clustering

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.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

Adaptive Web-Based Learning Framework

2011 ◽  
pp. 44-52
Author(s):  
Man-Hua Wu ◽  
Herng-Yow Chen
Keyword(s):  
World Wide ◽  
Learning Systems ◽  
The Internet ◽  
Knowledge Level ◽  
Educational Materials ◽  
Web Based ◽  
Course Content ◽  
Learning Framework ◽  
Web Based Learning ◽  
The World

With the rapid growth of the Internet and the increasing popularity of the World Wide Web, web-based learning systems have become more and more popular. However, in general Web-based learning systems, learners may often get lost in the enormous educational materials (Eirinaki & Vazirgiannis, 2003; Murray, 2002). This kind of situation refers to a disorientation problem. In addition to the disorientation problem, general Web-based learning systems provide every learner with uniform course content and presentation without considering the different knowledge level of learners. Therefore, the course content may be insufficient or unnecessary for learners with different knowledge level. This kind of situation was referred to as cognitive-overhead problem by Murray (2002).

Feedback adaptation in web-based learning systems

2007 ◽  
Vol 17 (4/5) ◽  
pp. 337 ◽  
Author(s):  
Ekaterina Vasilyeva ◽  
Seppo Puuronen ◽  
Mykola Pechenizkiy ◽  
Pekka Rasanen
Keyword(s):  
Learning Systems ◽  
Web Based ◽  
Web Based Learning

Hamming Distance based Clustering Algorithm

2012 ◽  
Vol 2 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Ritu Vijay ◽  
Prerna Mahajan ◽  
Rekha Kandwal
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Hamming Distance ◽  
Promising Result ◽  
Clustering Algorithms ◽  
Distribution Patterns ◽  
Mixed Data ◽  
Binary Representation ◽  
Data Sets ◽  
Performance Study

Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data.

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