{"id":1363,"date":"2014-08-11T12:29:21","date_gmt":"2014-08-11T12:29:21","guid":{"rendered":"http:\/\/sites.uac.pt\/amendes\/?p=1363"},"modified":"2014-08-11T12:29:21","modified_gmt":"2014-08-11T12:29:21","slug":"guide-data-mining","status":"publish","type":"post","link":"https:\/\/sites.uac.pt\/amendes\/estatistica\/guide-data-mining\/","title":{"rendered":"A Programmer’s Guide to Data Mining"},"content":{"rendered":"
\"Um<\/a>

Um livro on-line com alguns dos m\u00e9todos de data mining<\/p><\/div>\n

A guide to practical data mining, collective intelligence, and building recommendation systems by\u00a0Ron Zacharski<\/a>.<\/h3>\n

About This Book<\/h2>\n

Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don\u2019t get me wrong, the information in those books is extremely important. However, if you are a programmer interested in learning a bit about data mining you might be interested in a beginner\u2019s hands-on guide as a first step. That\u2019s what this book provides.
\nThis guide follows a learn-by-doing approach. Instead of passively reading the book, I encourage you to work through the exercises and experiment with the Python code I provide. I hope you will be actively involved in trying out and programming data mining techniques. The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques. This book is available for download for free under a Creative Commons license (see link in footer). You are free to share the book, and remix it. Someday I may offer a paper copy, but the online version will always be free.<\/p>\n

Table of Contents<\/h2>\n

This book\u2019s 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.<\/p>\n

Chapter 1: Introduction<\/a><\/h3>\n

Finding out what data mining is and what problems it solves. What will you be able to do when you finish this book.<\/p>\n

Chapter 2: Get Started with Recommendation Systems<\/a><\/h3>\n

Introduction to social filtering. Basic distance measures including Manhattan distance, Euclidean distance, and Minkowski distance. Pearson Correlation Coefficient. Implementing a basic algorithm in Python.<\/p>\n

Chapter 3: Implicit ratings and item-based filtering<\/a><\/h3>\n

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\u2013if they buy an mp3 from Amazon, we can view that purchase as a \u2018like\u2019 rating.<\/p>\n

Chapter 4: Classification<\/a><\/h3>\n

In \u00a0previous chapters we used \u00a0people\u2019s 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.<\/p>\n

Chapter 5: Further Explorations in Classification<\/a><\/h3>\n

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.<\/p>\n

Chapter 6: Na\u00efve Bayes<\/a><\/h3>\n

An exploration of Na\u00efve Bayes classification methods. Dealing with numerical data using probability density functions.<\/p>\n

Chapter 7: Na\u00efve Bayes and unstructured text<\/a><\/h3>\n

This chapter explores how we can use Na\u00efve 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?<\/p>\n

Chapter 8: Clustering<\/a><\/h3>\n

Clustering \u2013 both hierarchical and kmeans clustering.<\/p>\n","protected":false},"excerpt":{"rendered":"

A guide to practical data mining, collective intelligence, and building recommendation systems by\u00a0Ron Zacharski. About This Book Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don\u2019t get me wrong, the […]<\/p>\n","protected":false},"author":159,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[103,102,150],"tags":[120],"class_list":["post-1363","post","type-post","status-publish","format-standard","hentry","category-data-mining","category-estatistica","category-materiais-para-profissionais","tag-previsao"],"_links":{"self":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1363","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/users\/159"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/comments?post=1363"}],"version-history":[{"count":2,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1363\/revisions"}],"predecessor-version":[{"id":1366,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1363\/revisions\/1366"}],"wp:attachment":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/media?parent=1363"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/categories?post=1363"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/tags?post=1363"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}