A new approach in e-learners grouping using Hybrid Clustering Method

Today, learner grouping has an important role in sagacity of E-learning systems. In recent researches in this field, researchers have tried to improve basic grouping methods by combining them with optimization approaches. This complicates learner grouping methods and one dimensionality looking toward clustering, causes the quality of produced groups decreases. This paper proposes a new method based on feedback from basic clustering method such as Fuzzy C-means and K-means which it is called Hybrid Clustering Method (HCM). To judge of the best cluster, in HCM, based on cluster´s center proximity, a correspondence is made between similar clusters and then the best cluster is selected among similar clusters based on an index relating to cluster´s density concept. In the next stage, repetitive and eliminated elements are modified. This method looks at clustering problem as perspective of several different methods while maintaining the simplicity of basic cluster algorithms. The proposed method is evaluated and compared with other methods by using Purity and Gathering (P&G) index. The experimental results show that the proposed method has the best results comparing to the other methods such as Fuzzy C-means, K-means and Evolutionary Fuzzy C-means (EFC) in perceptual dimension of learners grouping.