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Mastering Clojure Data Analysis.
This book consists of a practical, example-oriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently. This book is great for those who have experience with Clojure and who need to use it to perform data analysis. This book will also be hugely benefici...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2014.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Network Analysis
- The Six Degrees of Kevin Bacon; Analyzing social networks; Getting the data; Understanding graphs; Implementing the graph; Loading the data; Measuring social network graphs; Density; Degrees; Paths; The average path length; Network diameter; Clustering coefficient; Centrality; Degrees of separation; Visualizing the graph; Setting up ClojureScript; A force-directed layout; A hive plot; A pie chart; Summary.
- Chapter 2: GIS Analysis
- Mapping Climate ChangeUnderstanding GIS; Mapping the climate change; Downloading and extracting the data; Downloading the files; Extracting the files; Transforming the data
- filtering; Rolling averages; Reading the data; Interpolating sample points and generating heat maps using inverse distance weighting (IDW); Working with map projections; Finding a base map; Working with ArcGIS; Summary; Chapter 3: Topic Modeling
- Changing Concerns in State of the Union Addresses; Understanding data in State of the Union addresses; Understanding topic modeling.
- Preparing for visualizationsSetting up the project; Getting the data; Loading the data into MALLET; Visualizing with D3 and ClojureScript; Exploring the topics; Exploring topic 43; Exploring topic 26; Exploring topic 42; Summary; Chapter 4: Classifying UFO Sightings; Getting the data; Extracting the data; Dealing with messy data; Visualizing UFO data; Description; Topic modeling descriptions; Hoaxes; Preparing the data; Reading the data into a sequence of data records; Splitting out the NUFORC comments; Categorizing the documents based on the comments.
- Partitioning the documents into directories based on the categoriesDividing them into training and test sets; Classifying the data; Coding the classifier interface; Running the classifier and examining the results; Summary; Chapter 5: Benford's Law
- Detecting Natural Progressions of Numbers; Learning about Benford's Law; Applying Benford's law to compound interest; Looking at the world population data; Failing Benford's Law; Case studies; Summary; Chapter 6: Sentiment Analysis
- Categorizing Hotel Reviews; Understanding sentiment analysis; Getting hotel review data; Exploring the data.
- Preparing the dataTokenizing; Creating feature vectors; Creating feature vector functions and POS tagging; Cross validating the results; Calculating error rates; Using the Weka machine learning library; Connecting Weka and cross validation; Understanding maximum entropy classifiers; Understanding naive Bayesian classifiers; Running the experiment; Examining the results; Combining the error rates; Improving the results; Summary; Chapter 7: Null Hypothesis Tests
- Analyzing Crime Data; Introducing confirmatory data analysis; Understanding null hypothesis testing; Understanding the process.