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This chapter aims to provide an up-to-date snapshot of the current state of research and practical applications of a specific type of data mining methods in e-learning, namely clustering methods, which address the problem of exploring and revealing the underlying grouping structure of the available data. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education-related publications are already beginning to pay attention to this new field. Data mining methods aim to bridge the fields of traditional statistics, pattern recognition and machine learning to provide analytical solutions to problems in all data-driven areas of research. Over the last few years, e-learning systems have gradually established themselves as a credible alternative to, and a complement of, traditional distance education models. They are also, by inception, data intensive. It is therefore understandable that both clustering data mining methods and e-learning practitioners are beginning to realize the full potential of their collaboration. We believe, though, that this is a promising collaborative avenue for research, in which clustering problems are likely to play a relevant role.