Clustering is one of the extensively used techniques in data mining to analyze a large dataset in order to discover useful and interesting patterns. It partitions a dataset into mutually disjoint groups of data in such a manner that the data points belonging to the same cluster are highly similar and those lying in different clusters are very dissimilar. Furthermore, among a large number of clustering algorithms, it becomes difficult for researchers to select a suitable clustering algorithm for their purpose. Keeping this in mind, this paper aims to perform a comparative analysis of various clustering algorithms such as k-means, expectation maximization, hierarchical clustering and make density-based clustering with respect to different parameters such as time taken to build a model, use of different dataset, size of dataset, normalized and un-normalized data in order to find the suitability of one over other.

The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and e-government among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.

Over the last few years, the face of traditional learning has changed significantly, due to the emergence of the web. Consequently several learning systems have emerged such as computer-based learning, web-based learning among others, meeting different kinds of educational needs of the learners and educators as well. E-learning systems allow educators, distribute information, create content material, prepare assignments, engage in discussions, and manage distance classes among others. They accumulate a huge amount of data as a result of learner’s interaction with the site. This data can be used to find students’ learning pattern based on which appropriate courses could be recommended to them. However existing approaches of recommending courses to learner offer the same course to all the learners irrespective of their knowledge and skill level which results in decreasing their academic performance. This paper proposes an architecture for the recommendation of courses to a learner based on his/her profile. The profile of a learner is created by applying k-means algorithm to learner’s interaction data in moodle. The results show that the non active learners should not be recommended advanced courses if they have obtained poor marks and are not active in the concern course.  In the initial stage we discover learners’ performance in data mining course which will further be extended to other courses as well.