最新章節(jié)
- Index
- Creating interactive visualizations with D3
- Visualizing graphs with force-directed layouts
- Creating time series charts with D3
- Creating histograms with NVD3
- Creating bar charts with NVD3
品牌：中圖公司
上架時間：2021-08-06 19:01:56
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Index 更新時間：2021-08-06 19:26:35
- Creating interactive visualizations with D3
- Visualizing graphs with force-directed layouts
- Creating time series charts with D3
- Creating histograms with NVD3
- Creating bar charts with NVD3
- Creating scatter plots with NVD3
- Setting up to use ClojureScript
- Creating HTML with Hiccup
- Serving data with Ring and Compojure
- Introduction
- Chapter 12. Creating Charts for the Web
- Creating dynamic charts with Incanter
- Using PCA to graph multi-dimensional data
- Saving Incanter graphs to PNG
- Customizing chart colors and styles
- Customizing charts with JFreeChart
- Adding lines to scatter charts
- Adding equations to Incanter charts
- Creating function plots with Incanter
- Creating histograms with Incanter
- Graphing non-numeric data in bar charts
- Creating scatter plots with Incanter
- Introduction
- Chapter 11. Graphing in Incanter
- Performing na?ve Bayesian classification with MALLET
- Performing topic modeling with MALLET
- Mapping documents to a sparse vector space representation
- Finding people places and things with Named Entity Recognition
- Scaling document frequencies with TF-IDF
- Scaling document frequencies by document size
- Getting document frequencies
- Focusing on content words with stoplists
- Finding sentences
- Tokenizing text
- Introduction
- Chapter 10. Working with Unstructured and Textual Data
- Finding associations in data with the Apriori algorithm
- Classifying data with support vector machines
- Classifying data with the Naive Bayesian classifier
- Classifying data with decision trees
- Clustering with SOMs in Incanter
- Finding hierarchical clusters in Weka
- Discovering groups of data using K-Means clustering
- Filtering renaming and deleting columns in Weka datasets
- Loading CSV and ARFF files into Weka
- Introduction
- Chapter 9. Clustering Classifying and Working with Weka
- Plotting in R from Clojure
- Evaluating R files from Clojure
- Passing vectors into R
- Calling R functions from Clojure
- Setting up R to talk to Clojure
- Creating functions from Mathematica
- Evaluating Mathematica scripts from Clojuratica
- Sending matrixes to Mathematica from Clojuratica
- Calling Mathematica functions from Clojuratica
- Setting up Mathematica to talk to Clojuratica for Windows
- Setting up Mathematica to talk to Clojuratica for Mac OS X and Linux
- Introduction
- Chapter 8. Working with Mathematica and R
- Finding data errors with Benford's law
- Modeling multinomial Bayesian distributions
- Modeling non-linear relationships
- Modeling linear relationships
- Validating sample statistics with bootstrapping
- Smoothing variables to decrease variation
- Working with time series data with Incanter Zoo
- Scaling variables to simplify variable relationships
- Working with changes in values
- Generating summary statistics with $rollup
- Introduction
- Chapter 7. Statistical Data Analysis with Incanter
- Projecting from multiple datasets with $join
- Saving datasets to CSV and JSON
- Grouping data with $group-by
- Filtering datasets with $where
- Selecting rows with $
- Selecting columns with $
- Using infix formulas in Incanter
- Converting datasets to matrices
- Viewing datasets interactively with view
- Loading Clojure data structures into datasets
- Loading Incanter's sample datasets
- Introduction
- Chapter 6. Working with Incanter Datasets
- Transforming data with Cascalog
- Composing Cascalog queries
- Defining new Cascalog operators
- Aggregating data with Cascalog
- Executing complex queries with Cascalog
- Parsing CSV files with Cascalog
- Distributing data with Apache HDFS
- Querying data with Cascalog
- Initializing Cascalog and Hadoop for distributed processing
- Introduction
- Chapter 5. Distributed Data Processing with Cascalog
- Benchmarking with Criterium
- Using type hints
- Generating online summary statistics for data streams with reducers
- Parallelizing with reducers
- Combining function calls with reducers
- Finding the optimal partition size with simulated annealing
- Partitioning Monte Carlo simulations for better pmap performance
- Parallelizing processing with Incanter
- Parallelizing processing with pmap
- Introduction
- Chapter 4. Improving Performance with Parallel Programming
- Managing large inputs with sized queues
- Recovering from errors in agents
- Debugging concurrent programs with watchers
- Monitoring processing with watchers
- Maintaining data consistency with validators
- Introducing safe side effects into the STM
- Maintaining consistency with ensure
- Combining agents and STM
- Getting better performance with commute
- Managing program complexity with agents
- Managing program complexity with STM
- Introduction
- Chapter 3. Managing Complexity with Concurrent Programming
- Validating data with Valip
- Parsing custom data formats
- Fixing spelling errors
- Sampling from very large data sets
- Lazily processing very large data sets
- Parsing dates and times
- Calculating relative values
- Regularizing numbers
- Identifying and removing duplicate data
- Maintaining consistency with synonym maps
- Cleaning data with regular expressions
- Introduction
- Chapter 2. Cleaning and Validating Data
- Aggregating data from different formats
- Querying RDF data with SPARQL
- Reading RDF data
- Scraping textual data from web pages
- Scraping data from tables in web pages
- Reading XML data into Incanter datasets
- Reading data from JDBC databases
- Reading data from Excel with Incanter
- Reading JSON data into Incanter datasets
- Reading CSV data into Incanter datasets
- Creating a new project
- Introduction
- Chapter 1. Importing Data for Analysis
- Customer support
- Reader feedback
- Conventions
- Who this book is for
- What you need for this book
- What this book covers
- Preface
- Support files eBooks discount offers and more
- www.PacktPub.com
- About the Reviewers
- About the Author
- Credits
- Clojure Data Analysis Cookbook Second Edition
- coverpage
- coverpage
- Clojure Data Analysis Cookbook Second Edition
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- 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
- Chapter 1. Importing Data for Analysis
- Introduction
- Creating a new project
- Reading CSV data into Incanter datasets
- Reading JSON data into Incanter datasets
- Reading data from Excel with Incanter
- Reading data from JDBC databases
- Reading XML data into Incanter datasets
- Scraping data from tables in web pages
- Scraping textual data from web pages
- Reading RDF data
- Querying RDF data with SPARQL
- Aggregating data from different formats
- Chapter 2. Cleaning and Validating Data
- Introduction
- Cleaning data with regular expressions
- Maintaining consistency with synonym maps
- Identifying and removing duplicate data
- Regularizing numbers
- Calculating relative values
- Parsing dates and times
- Lazily processing very large data sets
- Sampling from very large data sets
- Fixing spelling errors
- Parsing custom data formats
- Validating data with Valip
- Chapter 3. Managing Complexity with Concurrent Programming
- Introduction
- Managing program complexity with STM
- Managing program complexity with agents
- Getting better performance with commute
- Combining agents and STM
- Maintaining consistency with ensure
- Introducing safe side effects into the STM
- Maintaining data consistency with validators
- Monitoring processing with watchers
- Debugging concurrent programs with watchers
- Recovering from errors in agents
- Managing large inputs with sized queues
- Chapter 4. Improving Performance with Parallel Programming
- Introduction
- Parallelizing processing with pmap
- Parallelizing processing with Incanter
- Partitioning Monte Carlo simulations for better pmap performance
- Finding the optimal partition size with simulated annealing
- Combining function calls with reducers
- Parallelizing with reducers
- Generating online summary statistics for data streams with reducers
- Using type hints
- Benchmarking with Criterium
- Chapter 5. Distributed Data Processing with Cascalog
- Introduction
- Initializing Cascalog and Hadoop for distributed processing
- Querying data with Cascalog
- Distributing data with Apache HDFS
- Parsing CSV files with Cascalog
- Executing complex queries with Cascalog
- Aggregating data with Cascalog
- Defining new Cascalog operators
- Composing Cascalog queries
- Transforming data with Cascalog
- Chapter 6. Working with Incanter Datasets
- Introduction
- Loading Incanter's sample datasets
- Loading Clojure data structures into datasets
- Viewing datasets interactively with view
- Converting datasets to matrices
- Using infix formulas in Incanter
- Selecting columns with $
- Selecting rows with $
- Filtering datasets with $where
- Grouping data with $group-by
- Saving datasets to CSV and JSON
- Projecting from multiple datasets with $join
- Chapter 7. Statistical Data Analysis with Incanter
- Introduction
- Generating summary statistics with $rollup
- Working with changes in values
- Scaling variables to simplify variable relationships
- Working with time series data with Incanter Zoo
- Smoothing variables to decrease variation
- Validating sample statistics with bootstrapping
- Modeling linear relationships
- Modeling non-linear relationships
- Modeling multinomial Bayesian distributions
- Finding data errors with Benford's law
- Chapter 8. Working with Mathematica and R
- Introduction
- Setting up Mathematica to talk to Clojuratica for Mac OS X and Linux
- Setting up Mathematica to talk to Clojuratica for Windows
- Calling Mathematica functions from Clojuratica
- Sending matrixes to Mathematica from Clojuratica
- Evaluating Mathematica scripts from Clojuratica
- Creating functions from Mathematica
- Setting up R to talk to Clojure
- Calling R functions from Clojure
- Passing vectors into R
- Evaluating R files from Clojure
- Plotting in R from Clojure
- Chapter 9. Clustering Classifying and Working with Weka
- Introduction
- Loading CSV and ARFF files into Weka
- Filtering renaming and deleting columns in Weka datasets
- Discovering groups of data using K-Means clustering
- Finding hierarchical clusters in Weka
- Clustering with SOMs in Incanter
- Classifying data with decision trees
- Classifying data with the Naive Bayesian classifier
- Classifying data with support vector machines
- Finding associations in data with the Apriori algorithm
- Chapter 10. Working with Unstructured and Textual Data
- Introduction
- Tokenizing text
- Finding sentences
- Focusing on content words with stoplists
- Getting document frequencies
- Scaling document frequencies by document size
- Scaling document frequencies with TF-IDF
- Finding people places and things with Named Entity Recognition
- Mapping documents to a sparse vector space representation
- Performing topic modeling with MALLET
- Performing na?ve Bayesian classification with MALLET
- Chapter 11. Graphing in Incanter
- Introduction
- Creating scatter plots with Incanter
- Graphing non-numeric data in bar charts
- Creating histograms with Incanter
- Creating function plots with Incanter
- Adding equations to Incanter charts
- Adding lines to scatter charts
- Customizing charts with JFreeChart
- Customizing chart colors and styles
- Saving Incanter graphs to PNG
- Using PCA to graph multi-dimensional data
- Creating dynamic charts with Incanter
- Chapter 12. Creating Charts for the Web
- Introduction
- Serving data with Ring and Compojure
- Creating HTML with Hiccup
- Setting up to use ClojureScript
- Creating scatter plots with NVD3
- Creating bar charts with NVD3
- Creating histograms with NVD3
- Creating time series charts with D3
- Visualizing graphs with force-directed layouts
- Creating interactive visualizations with D3
- Index 更新時間：2021-08-06 19:26:35
