The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of online content and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only a few works have been proposed to distinguish between prerequisite and outcome concepts, and to the best of our knowledge, no method has been introduced so far to benefit from this information to produce a meaningful learning path. To this aim, this article first describes a concept annotation method that relies on machine-learning techniques to predict the class of each concept—prerequisite or outcome—on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resource sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real dataset built from online resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the online content.

This paper proposes two new incremental fuzzy c medoids clustering algorithms for very large datasets. These algorithms are tailored to work with continuous data streams, where all the data is not necessarily available at once or can not fit in main memory. Some fuzzy algorithms already propose solutions to manage large datasets in a similar way but are generally limited to spatial datasets to avoid the complexity of medoids computation. Our methods keep the advantages of the fuzzy approaches and add the capability to handle large relational datasets by considering the continuous input stream of data as a set of data chunks that are processed sequentially. Two distinct models are proposed to aggregate the information discovered from each data chunk and produce the final partition of the dataset. Our new algorithms are compared to state-of-the-art fuzzy clustering algorithms on artificial and real datasets. Experiments show that our new approaches perform closely if not better than existing algorithms while adding the capability to handle relational data to better match the needs of real world applications.

K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seed based K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or they are assumed to be available for each cluster. This paper introduces a new efficient algorithm for active seeds selection which relies on a Min-Max approach that favors the coverage of the whole dataset. Experiments conducted on artificial and real datasets show that, using our active seeds selection algorithm, each cluster contains at least one seed after a very small number of queries and thus helps reducing the number of iterations until convergence which is crucial in many KDD applications.