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Table of Contents
This book’s contents are freely available as PDF files. When you click on a chapter title below, you will be taken to a webpage for that chapter. The page contains links for a PDF of that chapter and for any sample Python code and data that chapter requires. Please let me know if you see an error in the book, if some part of the book is confusing, or if you have some other comment. I will use these to revise the chapters.
Chapter 1: Introduction
Finding out what data mining is and what problems it solves. What will you be able to do when you finish this book.
Chapter 2: Get Started with Recommendation Systems
Introduction to social filtering. Basic distance measures including Manhattan distance, Euclidean distance, and Minkowski distance. Pearson Correlation Coefficient. Implementing a basic algorithm in Python.
Chapter 3: Implicit ratings and item-based filtering
A discussion of the types of user ratings we can use. Users can explicitly give ratings (thumbs up, thumbs down, 5 stars, or whatever) or they can rate products implicitly–if they buy an mp3 from Amazon, we can view that purchase as a ‘like’ rating.
Chapter 4: Classification
In previous chapters we used people’s ratings of products to make recommendations. Now we turn to using attributes of the products themselves to make recommendations. This approach is used by Pandora among others.
Chapter 5: Further Explorations in Classification
A discussion on how to evaluate classifiers including 10-fold cross-validation, leave-one-out, and the Kappa statistic. The k Nearest Neighbor algorithm is also introduced.
Chapter 6: Naïve Bayes
An exploration of Naïve Bayes classification methods. Dealing with numerical data using probability density functions.
Chapter 7: Naïve Bayes and unstructured text
This chapter explores how we can use Naïve Bayes to classify unstructured text. Can we classify twitter posts about a movie as to whether the post was a positive review or a negative one? (new version coming November 2013)
Mahout currently hasCollaborative FilteringUser and Item based recommendersK-Means, Fuzzy K-Means clusteringMean Shift clusteringDirichlet process clusteringLatent Dirichlet AllocationSingular value decompositionParallel Frequent Pattern miningComplementary Naive Bayes classifierRandom forest decision tree based classifierHigh performance java collections (previously colt collections)A vibrant communityand many more cool stuff to come by this summer thanks to Google summer of code
Don’t take your data at face value. That is the key message of this tutorial which focuses on how scholars can diagnose and act upon the accuracy of data. In this lesson, you will learn the principles and practice of data cleaning, as well as how OpenRefine can be used to perform four essential tasks that will help you to clean your data:
Remove duplicate recordsSeparate multiple values contained in the same fieldAnalyse the distribution of values throughout a data setGroup together different representations of the same reality
These steps are illustrated with the help of a series of exercises based on a collection of metadata from the Powerhouse museum, demonstrating how (semi-)automated methods can help you correct the errors in your data.
Hyphe does not manage different corpora or users at the moment. All the data is stored as a single corpus summarized here.