Abstract. Personalized service is becoming increasingly important, especially in E-learning field. Most personalized E-learning systems only take learners preferences, interests and browsing behaviors into consideration. These systems usually neglect considering whether the learners ability and the difficulty level of recommended learning materials are matched to each other or not. This paper proposes a personalized E-learning system using fuzzy set based clustering algorithm which considers both course materials' difficulty and learners' ability to provide appropriate learning stuffs for learners individually, to help learners learn more efficiently and effectively.

In this paper, we propose a methodology based on unsupervised learning for automatic clustering of wafer spatial signatures to aid yield improvement. Our proposed methodology is based on three steps. First, we apply sparse regression to automatically capture wafer spatial signatures by a small number of features. Next, we apply an unsupervised hierarchical clustering algorithm to divide wafers into a few clusters where all wafers within the same cluster are similar. Finally, we develop a modified L-method to determine the appropriate number of clusters from the hierarchical clustering result. The accuracy of the proposed methodology is demonstrated by several industrial data sets of silicon measurements.

Recommender system (RS) clustering is an important issue, both for the improvement of the collaborative filtering (CF) accuracy and to obtain analytical information from their high sparse datasets. RS items and users usually share features belonging to different clusters, e.g., a musical-comedy movie. Soft clustering, therefore, is the CF clustering's most natural approach. In this paper, we propose a new prediction approach for probabilistic soft clustering methods. In addition, we put to test a not traditional scientific documentation CF dataset: SD4AI, and we compare results with the MovieLens baseline. Not traditional CF datasets have challenging features, such as not regular rating frequency distributions, broad range of rating values, and a particularly high sparsity. The results show the suitability of using soft-clustering approaches, where their probabilistic overlapping parameters find optimum values when balanced hard/soft clustering is used. This paper opens some promising lines of research, such as RSs' use in the scientific documentation field, the Internet of Things-based datasets processing, and design of new model-based soft clustering methods. INDEX TERMSSoft clustering, scientific documentation, collaborative filtering, recommender systems. JESÚS BOBADILLA received the B.S. degree in computer science from the Universidad Politécnica de Madrid and the Ph.D. degree in computer science from Universidad Carlos III.

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