KNIME Essentials. Perform accurate data analysis using the power of KNIME - Helion
Tytuł oryginału: KNIME Essentials. Perform accurate data analysis using the power of KNIME
ISBN: 9781849699228
stron: 148, Format: ebook
Data wydania: 2013-10-16
Księgarnia: Helion
Cena książki: 109,00 zł
KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R.
"KNIME Essentials" teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports.
"KNIME Essentials" guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.
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Spis treści
KNIME Essentials. Perform accurate data analysis using the power of KNIME eBook -- spis treści
- KNIME Essentials
- Table of Contents
- KNIME Essentials
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Support files, eBooks, discount offers and more
- Why Subscribe?
- Free Access for Packt account holders
- Support files, eBooks, discount offers and more
- 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
- Errata
- Piracy
- Questions
- 1. Installing and Using KNIME
- Few words about KNIME
- Installing KNIME
- Installation using the archive
- KNIME for Windows
- KNIME for Linux
- KNIME for Mac OS X
- Troubleshooting
- Installation using the archive
- KNIME terminologies
- Organizing your work
- Nodes
- Node lifecycle
- Meta nodes
- Ports
- Data tables
- Port view
- Flow variables
- Node views
- HiLite
- Eclipse concepts
- Preferences
- Logging
- User interface
- Getting started
- Setting preferences
- KNIME
- Other preferences
- Installing extensions
- Workbench
- Workflow handling
- Node controls
- HiLite
- Variable flows
- Meta nodes
- Workflow lifecycle
- Other views
- Summary
- 2. Data Preprocessing
- Importing data
- Importing data from a database
- Starting Java DB
- Importing data from tabular files
- Importing data from web services
- REST services
- Importing XML files
- Importing models
- Other formats
- Public data sources
- Importing data from a database
- Regular expressions
- Basic syntax
- Partial versus whole match
- Usage from Java
- References and tools
- Alternative pattern description
- Transforming the shape
- Filtering rows
- Sampling
- Appending tables
- Less columns
- Dimension reduction
- More columns
- GroupBy
- Pivoting and Unpivoting
- One2Many and Many2One
- Cosmetic transformations
- Renames
- Changing the column order
- Reordering the rows
- The row ID
- Transpose
- Filtering rows
- Transforming values
- Generic transformations
- Java snippets
- The Math Formula node
- Conversion between types
- Binning
- Normalization
- Text normalization
- Regular expressions
- Text normalization
- Multiple columns
- XML transformation
- Time transformation
- Smoothing
- Generic transformations
- Data generation
- Generating the grid
- Constraints
- Loops
- Workflow customization
- Case study finding min-max in the next n rows
- Case study ranks within groups
- Summary
- Importing data
- 3. Data Exploration
- Computing statistics
- Overview of visualizations
- Visual guide for the views
- Distance matrix
- Using visual properties
- Color
- Size
- Shape
- KNIME views
- HiLite
- Use cases for HiLite
- Row IDs
- Extreme values
- HiLite
- Basic KNIME views
- The Box plots
- Hierarchical clustering
- Histograms
- Interactive Table
- The Lift chart
- Lines
- Pie charts
- The Scatter plots
- Spark Line Appender
- Radar Plot Appender
- The Scorer views
- JFreeChart
- The Bar charts
- The Bubble chart
- Heatmap
- The Histogram chart
- The Interval chart
- The Line chart
- The Pie chart
- The Scatter plot
- Open Street Map
- 3D Scatterplot
- Other visualization nodes
- The R plot, Python plot, and Matlab plot
- The official R plots
- The RapidMiner view
- The HiTS visualization
- Tips for HiLiting
- Using Interactive HiLite Collector
- Finding connections
- Visualizing models
- Further ideas
- Summary
- 4. Reporting
- Installation of the reporting extensions
- Reporting concepts
- Importing data
- Sending data and images to a report
- Importing from other sources
- Joining data sets
- Preferences
- Using the designer
- In visible views
- Report properties
- Report items
- Label
- Text
- Binding
- Dynamic text
- Data
- Image
- Grid
- List
- Groups
- Sorting
- Filters
- Table
- Chart
- Cross Tab
- Setting up
- Changing
- Using data cubes
- Quick Tools
- Aggregation
- Relative time period
- Generating reports
- Using colors
- Using HiLite
- Using workflow variables
- Suggested readings
- Summary
- Index