Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar
Lecture slides (in both PPT and PDF formats) and three sample Chapters on classification, association and clustering available at the above link.

Mining of Massive Datasets by Anand Rajaraman and Jeff Ullman
The whole book and lecture slides are free and downloadable in PDF format.

Lecture notes of data mining course by Cosma Shalizi at CMU
R code examples are provided in some lecture notes, and also in solutions to home works.
It covers information retrieval, page rank, image search, information theory, categorization, clustering, transformations, principal components, factor analysis, nonlinear dimensionality reduction, regression, classification and regression trees, support vector machines, density estimation, mixture models, causal inference, etc.

Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze at Stanford University
It covers text classification, clustering, web search, link analysis, etc. The book and lecture slides are free and downloadable in PDF format.

Text Mining Tutorial
It introduces various techniques at different levels of text processing, including word level, sentence level, document level and document-collection level. It covers stemming, stop words, document summarization, visualization, segmentation, categorization and clustering.

State of the Art in Parallel Computing with R, provides an excellent overview and comparison of R packages for parallel computing, including packages for computer cluster, grid computing and multi-core systems