Exploring data with RapidMiner : explore, understand, and prepare real data using rapidminer's practical tips and tricks /: explore, understand, and prepare real data using rapidminer's practical tips and tricks. (2013)
- Record Type:
- Book
- Title:
- Exploring data with RapidMiner : explore, understand, and prepare real data using rapidminer's practical tips and tricks /: explore, understand, and prepare real data using rapidminer's practical tips and tricks. (2013)
- Main Title:
- Exploring data with RapidMiner : explore, understand, and prepare real data using rapidminer's practical tips and tricks
- Further Information:
- Note: Andrew Chisholm.
- Authors:
- Chisholm, Andrew, 1959-
- Contents:
- Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Setting the Scene; A process framework; Data volume and velocity; Datavariety, formats, and meanings; Missing data; Cleaning data; Visualizing data; Resource constraints; Terminology; Accompanying material; Summary; Chapter 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. Chapter 3: Visualizing DataGetting 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; Chapter 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; Chapter 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; Chapter 6: Missing Values; Missing or empty?;Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Setting the Scene; A process framework; Data volume and velocity; Datavariety, formats, and meanings; Missing data; Cleaning data; Visualizing data; Resource constraints; Terminology; Accompanying material; Summary; Chapter 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. Chapter 3: Visualizing DataGetting 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; Chapter 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; Chapter 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; Chapter 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 causeIgnore it; Manual editing; Deletion of examples; Deletion of attributes; Imputation with single values; Modeling; Summary; Chapter 7: Transforming Data; Creating new attributes; Aggregation; Using pivoting; Using de-pivoting; Summary; Chapter 8: Reducing Data Size; Removing examples using sampling; Removing attributes; Removing useless attributes; Weighting attributes; Selecting attributes using models; Summary; Chapter 9: Resource Constraints; Measuring and estimating performance; Measuring performance; Adding memory; Parallel processing; Restructuring processes. … (more)
- Publisher Details:
- Birmingham, UK : Packt Publishing
- Publication Date:
- 2013
- Extent:
- 1 online resource (iv, 148 pages), illustrations
- Subjects:
- 006.3
COMPUTERS -- Programming Languages -- Java
Data mining
COMPUTERS -- General
COMPUTERS -- Databases -- Data Warehousing
Electronic books - Languages:
- English
- ISBNs:
- 9781782169345
1782169342 - Related ISBNs:
- 9781782169338
1782169334 - Notes:
- Note: Print version record.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.86839
- Ingest File:
- 01_091.xml