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- Title An Introduction to Data Mining
- Author(s) Dr. Saed Sayad
- Publisher: University of Toronto (2010-Date)
- Paperback N/A
- eBook Online, HTML
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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This book presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
It provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.
About the Authors- N/A
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Mining Social Media: Finding Stories in Internet Data
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This book uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.
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A Programmer's Guide to Data Mining (Ron Zacharski)
This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's hands-on guide as a first step. That's what this book provides.
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Advanced Data Analysis from an Elementary Point of View
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Mining of Massive Datasets (Jure Leskovec, et al)
It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.
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Bayesian Data Analysis (Andrew Gelman, et al.)
This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.
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Data Mining and Analysis: Fundamental Concepts and Algorithms
This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.
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Answering Questions with Data (Matthew Crump, et al.)
This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.
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Exploratory Data Analysis with R (Roger D. Peng)
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.
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Basic Data Analysis and More - A Guided Tour using Python
In this book, a selection of frequently required statistical tools will be introduced and illustrated. An exemplary implementation of the presented techniques using the Python programming language is provided.
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Text Mining with R: A Tidy Approach (Julia Silge, et al)
You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
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Theory and Applications for Advanced Text Mining (S. Sakurai)
This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.
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