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

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

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

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

Introduction | Markov Decision Processes | Definition | Notation Used in the Package | Package Functionality | Defining an MDP Problem | Solving a 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 a Gridworld | Working wit Gridworld MDPs | Solving a Gridworld | Experimenting with Solvers

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 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

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

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

Introduction | Using Sockets Directly to Publish a Data Stream | Server: Serving a Data Stream | Client: Reading from the Stream | Server: Stoping the Server Process | streamConnect Sockets | Server: Publish Data | Client: Connect to the Data Stream | streamConnect Web Services | Server: Create a Web Service | Client: Connect to the Web Service | Server: Stop the Web Service | Deploying the Server/Web Service

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

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

Introduction | Experimental Comparison on Static Data | Outlier detection

Introduction | System requirements | Examples | Use an existing database | Create a custom BLAST database | SessionInfo

Introduction | System requirements | Examples | Use an existing database | Create a custom BLAST database | SessionInfo

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

Extending the stream framework

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