Exploring Data with RapidMiner -- Table of Contents -- Exploring Data with RapidMiner -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Downloading the color images of this book -- Errata -- Piracy -- Questions -- 1. Setting the Scene -- A process framework -- Data volume and velocity -- Data variety, formats, and meanings -- Missing data -- Cleaning data -- Visualizing data -- Resource constraints -- Terminology -- Accompanying material -- Summary -- 2. Loading Data -- Reading files -- Alternative delimiters -- Reading complete lines -- Reading large numbers of attributes -- Splitting files into smaller pieces -- Databases -- The Read Database operator -- Large datasets -- Using macros -- Summary -- 3. Visualizing Data -- Getting started -- Statistical summaries -- Relationships between attributes -- Scatter plots -- Scatter 3D color -- Parallel and deviation -- Quartile color -- Time series data -- Plotting series -- Using the survey plotter -- Relations between examples -- Using histograms -- Using block plots -- Summary -- 4. Parsing and Converting Attributes -- Generating attributes -- Date functions -- Regular expression functions -- Generating extracts -- Regular expressions -- XPath -- Renaming attributes -- Searching and replacing attribute values -- Using the Map operator -- Using the Replace operator -- Using Replace (Dictionary) -- Summary -- 5. Outliers -- Manual inspection -- Increasing the data volume -- Rules for handling outliers -- Automated detection of example outliers -- Detect Outlier (Distances).

Detect Outlier (Densities) -- Detect Outlier (LOF) -- Detect Outliers (COF) -- Summary -- 6. Missing Values -- Missing or empty? -- Types of missing data -- Missing completely at random -- Missing at random -- Not missing at random -- Categorizing missing data -- Finding MCAR data -- Finding MAR data -- Finding NMAR data -- A cautionary note -- Effect of missing data -- Options for handling missing data -- Returning to the root cause -- Ignore it -- Manual editing -- Deletion of examples -- Deletion of attributes -- Imputation with single values -- Modeling -- Summary -- 7. Transforming Data -- Creating new attributes -- Aggregation -- Using pivoting -- Using de-pivoting -- Windowing data -- Summary -- 8. Reducing Data Size -- Removing examples using sampling -- Removing attributes -- Removing useless attributes -- Weighting attributes -- Selecting attributes using models -- Summary -- 9. Resource Constraints -- Measuring and estimating performance -- Measuring performance -- Adding memory -- Parallel processing -- Restructuring processes -- Summary -- 10. Debugging -- Breakpoints in RapidMiner Studio -- Logging data in RapidMiner Studio -- RapidMiner Studio console printing -- Groovy scripts -- Outputting macros example -- Console logging with Groovy -- Regex tools -- Using XPath effectively -- Summary -- 11. Taking Stock -- Exploring new techniques -- Time series -- Web mining -- Using R -- Java or Groovy -- Third-party components -- RapidMiner Server -- Where to go next -- Index.