Multidimensional scaling of interval-valued dissimilarity data
Pattern Recognition Letters
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Fuzzy multidimensional scaling
Computational Statistics & Data Analysis
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Analyzing the data that is collected in a knowledge survey serves the teacher for determining the student's learning needs at the beginning of the course and for finding a relationship between these needs and the capacities acquired during the course In this paper we propose using graphical exploratory analysis for projecting all the data in a map, where each student will be placed depending on his/her knowledge profile, allowing the teacher to identify groups with similar background problems, segment heterogeneous groups and perceive the evolution of the abilities acquired during the course. The main innovation of our approach consists in regarding the answers of the tests as imprecise data We will consider that either a missing or unknown answer, or a set of conflictive answers to a survey, is best represented by an interval or a fuzzy set This representation causes that each individual in the map is no longer a point but a figure, whose shape and size determine the coherence of the answers and whose position with respect to its neighbors determine the similarities and differences between the students.