Introduction | Extensible Markov model | Threshold Nearest Neighbor clustering algorithm | Implementation details | Examples | Applications | Conclusion

Introduction | Cluster the observations | Add the temporal model

Introduction | Prepare the stream and models | Update both models | Inspect the temporal model | Predict the next state | Start an independent sequence | Use other stream clusterers | References

Introduction | Install and load the package | Prepare an ordered sequence | Build and inspect an EMM | Simplifying the model | Score a new sequence | Predict a future state | Add observations incrementally | Next steps | References

Installation | Seriate a dissimilarity matrix | Assess the quality of a seriation order | Seriate a data matrix | Choose a method | Where to go next

Methods for dissimilarity data (dist) | Methods for data matrices, tables and data.frames | References

Introduction | Prepare the data | Visualization options | Using other seriation methods | Other packages | Package corrgram | Package corrr | Package corrplot | References

Introduction | Prepare the data | Reordering in seriation | Built-in heatmap function | Without dendrograms: pimage | With dendrograms: hmap | With distance matrices instead of dendrograms: hmap | colors with pimage and hmap | ggplot2 | Using seriation with other packages | heatmap in package stats | Package heatmaply

Introduction | Seriation as a combinatorial optimization problem | Seriation methods | The package infrastructure | Examples and applications | Conclusion

Introduction | Examples | Ruspini: A simple dataset | Votes: High dimensional data | Conclusion | References

Load the data | Fit the baseline model | Mine NB-frequent itemsets | NB-frequent vs. frequent itemsets? | Mine NB-precise rules | References

Introduction | Collaborative Filtering | Evaluation of Recommender Algorithms | Recommenderlab Infrastructure | Examples | Conclusion

Install and load | Read a data stream over a socket | Update a remote clusterer over a web service | Publish a stream through a web service | Next steps

Installation | Create a data stream | Cluster the stream | Inspect the result

Introduction | Experimental Comparison on Static Data | Outlier detection

Installation | Create and inspect a data stream | Update a clustering model | Evaluate on new points | Further reading

Extending the stream framework

Introduction | Data stream mining | The stream framework | Data stream data (DSD) | Data stream task (DST) | Evaluation of data stream clustering | Example applications | Conclusion and future work

Installation and System Requirements | How to cite this package | Classification with RDP | Training a custom RDP classifier | Session Info | Acknowledgments | References

Installation | Load and prepare ratings | Fit a recommender and make recommendations | Evaluate recommendations | Where to go next

Introduction | Markov Decision Processes | Definition | Notation Used in the Package | Package Functionality | Defining an MDP Problem | Solving an MDP | Toy Example: Steward Russell's 4x3 Maze Gridworld MDP | Specifying the Stochastic Maze | Solving the Maze | Additional Functions | Access to Model Components | Value Function | Policy | Evaluation | Sampling | Acknowledgments | References

Introduction | Linear Approximation | State-action Feature Vector Construction | Helper Functions | Episodic Semi-gradient Sarsa | Example 1: A maze without walls | Example 2: Stuart Russell's 3x4 Maze using Linear Approximation | Linear Basis | Order-1 Polynomial Basis | Radial Basis Function | Order-1 Fourier Basis | Example 3: Wall Maze | References

Introduction | Defining Tic-Tac-Toe as an MDP | Actions | State Space | Transition Model | Reward Function | Constructing the MDP | Define Test Boards | Solve | LP | Value Iteration | Policy Iteration | Q-Learning | References

Installation | Create transactions | Mine and inspect rules | More vignettes

Example data | k nearest neighbors | Reuse and reduce a search | Query new points | Fixed-radius nearest neighbors | Reduce a stored radius | Shared nearest neighbors | Neighbor-distance diagnostics | Connected components | Sorting results | Using non-Euclidean distances | Search strategies and performance | Important details | Reference

Hierarchical DBSCAN | DBSCAN* vs cutting the HDBSCAN* tree | Simplified Tree | Cluster Stability Scores | Global-Local Outlier Score from Hierarchies | A Larger Clustering Example | Performance | References

Example data | Low point density | Local Outlier Factor (LOF) | GLOSH and HDBSCAN | Turning scores into candidates | DBSCAN noise is not an outlier score | Practical workflow | References

Shared nearest neighbors | Example data | Jarvis--Patrick clustering | Shared nearest neighbor clustering | Reuse a nearest-neighbor search | Inspect the shared-neighbor graph | Use a precomputed distance matrix | Which method should you use? | References

Create an OPTICS ordering | Read the reachability plot | Choose minPts and eps | Extract a DBSCAN-like clustering | Extract clusters with the Xi method | Convert the ordering to a dendrogram | Predict labels for new observations | Use another distance measure | References

Preamble | Introduction | Density-based clustering | The dbscan package | Performance Comparison | Concluding Remarks | Acknowledgments | Technical Note on OPTICS cluster extraction

Preamble | Introduction | Theory | Computational infrastructure: the TSP package | Examples | Conclusion

Preamble | Introduction | Data structure overview | Mining algorithm interfaces | Auxiliary functions | Examples | Summary and outlook

Introduction | Data preparation and unified interface of arulesViz | Scatter plot | Matrix-based visualizations | Grouped matrix-based visualization | Graph-based visualizations | Parallel coordinates plot | Double Decker plots | Comparison of techniques | Further Reading | Conclusion

Installation | Train a classifier | Inspect the learned rules | Predict and evaluate | Control rule mining | Prepare and mine rules separately | Other classifiers

Installation | Create a set of rules | Start with an overview | Focus on interesting rules | Interactive exploration | Visualize frequent itemsets

Introduction | An Example MDP | Solving an Infinite-Horizon MDP | Choosing Dynamic Programming Parameters | Finite-Horizon Problems | Learning from Simulated Experience | Evaluating and Simulating a Policy | Next Steps

Installation | A first clustering | Prepare your own data | Choose minPts and eps | Assign new observations | When DBSCAN is not the best starting point | Next steps

Standard interest measures | Calculating additional measures for rules

Aggregating transactions | Mine across hierarchy levels

Constrain the search | Rank and filter | Remove redundant rules

From a list of baskets | From a binary matrix | From a data frame in wide format | From a data frame in long format

Criteria for dissimilarity data (dist) | Criteria for data matrices, tables and data.frames | References

Introduction | Distance seriation | Comparison between methods | Visualize the results | Matrix seriation

Introduction | Numeric Example

Introduction | Partially Observable Markov Decision Processes | Package Functionality | Defining a POMDP Problem | Solving a POMDP | The Tiger Problem Example | Specifying the Tiger Problem | Solving the Tiger Problem | Visualization | References

Introduction | Defining a Gridworld | Working with Gridworld MDPs | Solving a Gridworld | Experimenting with Solvers

Overview | System requirements | Quick start: build and query a local database | Multiple queries and custom output | Threads and parallel searches | Temporary files and troubleshooting | Prebuilt NCBI databases | Remote searches | Session information

Overview | System requirements | Quick start: build and query a local database | Multiple queries and custom output | Threads and parallel searches | Temporary files and troubleshooting | Prebuilt NCBI databases | Remote searches | Session information

Introduction | Single numeric input | Single categorical input | Multiple input fields | Using a previously derived feature | Factor output | PMML functions supported by xform_function | PMML functions not supported by xform_function | PMML function not supported by xform_function - another example | PMML for arbitrary functions | More notes on functions | More examples of functions | References

Introduction | Supported packages | Data transformations | Additional functions | References

Introduction | Defining a Gridworld | Working with Gridworld MDPs | Solving a Gridworld | Experimenting with Solvers | References