Data Mining: The Textbook, Springer, May 2015
Charu C. Aggarwal.
Comprehensive textbook on data mining: Table of Contents
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The emergence of data science as a discipline requires the development of a book that goes beyond the traditional focus of books on fundamental data mining problems. More emphasis needs to be placed on the advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. This comprehensive data mining book explores the different aspects of data mining, starting from the fundamentals, and subsequently explores the complex data types and their applications. Therefore, this book may be used for both introductory and advanced data mining courses. The chapters of this book fall into one of three categories:
The fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.
Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.
Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.
The book carefully balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners. Numerous illustrations, examples, and exercises are included with an emphasis on semantically interpretable examples.
Cost-effective methods for obtaining electronic and hardcopy versions
The book is available in both hardcopy (hardcover) and electronic versions. The hardcover is available at all the usual channels (e.g, Amazon, Barnes and Noble etc.), in Kindle format, and also directly from Springer in hardcopy and pdf format. The good thing about Springer is that electronic versions are often widely accessible at no cost to subscribing institutions, which is particularly convenient for students. My understanding is that a very large fraction of universities in North America, Europe, Australia, and New Zealand are subscribers, and a rapidly increasing number of universities in Asia are also subscribing. The electronic version is available at the following Springerlink pointer . For subscribing institutions click from a computer directly connected to your institution network to download the book for free. Springer uses the domain name of your computer to regulate access. To be eligible, your institution must subscribe to "e-book package english (Computer Science)" or "e-book package english (full collection)". If your institution is eligible, you will see a (free) `Download Book' button. Otherwise you will see a (paid) `Get Access' button. Sometimes you may be able to download it from your library e-collection, even when it is not Web-accessible from your institution. For those who prefer desk copies rather than electronic books, there are some very cost-effective methods to obtain a paperback MyCopy edition for $25 or less (subscribing institutions only). If you have ever published an article (even journal) with Springer, you are also entitled to an additional 40% author discount for any Springer book (including the $25 paperback edition) using the approach described here .
In general, for electronic versions, I highly recommend buying the PDF directly from springer over Amazon's Kindle edition. The PDF has embedded links that allows navigation over an e-reader, and will take about 18 MB on your device. Aside from this, one PDF allows you use over any device or computer. Since the PDFs are fully produced by Springer (rather than Amazon kindle, where Amazon plays a role in conversion), the look and feel is fully controlled by author and publisher. This makes the PDF versions of better quality than an Amazon Kindle.
About the Author
Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He has published 14 (3 authored and 11 edited) books, over 250 papers in refereed venues, and has applied for or been granted over 80 patents. His h-index is 70. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of an IBM Research Division Award (2008) for his scientific contributions to data stream research. He has received two best paper awards and an EDBT Test-of-Time Award (2014). He has served as the general or program co-chair of the IEEE Big Data Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference (2015), and the KDD Conference (2016). He also co-chaired the data mining track at the WWW Conference 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Knowledge Discovery and Data Mining Journal , an action editor of the Data Mining and Knowledge Discovery Journal , an associate editor of the IEEE Transactions on Big Data, and an associate editor of the Knowledge and Information Systems Journal. He is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the SIAM (2015), ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining techniques."
Solution Manual for Book
The solution manual for the book is available here from Springer. There is a link for the solution manual on this page. If you are an instructor, then you can obtain a copy. Please do not ask me directly for a copy of the solution manual. It can only be distributed by Springer.
Resources for book
The resources for this book will grow over time. Currently, I have not found time to prepare slides for
teaching and will add them over time. I will also make the powerpoint figures of the book available soon. Meanwhile, I have added links to various sites on the internet where software is available
for related material. In case you use the book and prepare slides, please try to share them on the internet. I can add a link from
my site like the links below (with your name acknowledged of course).
Chapter 1: An Introduction to Data Mining
Data Sets from UCI Machine Learning Repository
Open Source Data Mining Software (WEKA Workbench)
Apache Mahout Machine Learning Library
Spider Machine Learning Library (MATLAB)
KD-Nuggets: Resources in Data Mining
Chapter 2: Data Preparation
Data Preparator from Bobbie Stewart
Scikit-learn Data Preparation (Python)
Scikit-learn Dimensionality Reduction (Python)
PCA, SVD, and eigen-decomposition implementation by redSVD
Various forms of matrix decomposition implementations including SVD by ALGLIB
Haar Wavelet Implementation by Tom Gibara
Multidimensional Scaling (MDS) implementation from University of Konstanz
ISOMAP from Stanford University
Weka Matrix class for Matrix Operations such as SVD
Chapter 3: Similarity and Distances
ISOMAP from Stanford University
Dynamic Time Warping by D. Ellis
Longest Common Subsequence at Rosetta Code
Chapters 4 and 5: Association Pattern Mining
SPMF Frequent Pattern Mining Implementations
Open Source Data Mining Software (WEKA Workbench)
FIMI Implementations on Frequent Pattern Mining
Implementations by Christian Borgelt
Chapters 6 and 7: Data Clustering
Scikit-learn Data Clustering (Python)
Open Source Data Mining Software (WEKA Workbench)
Apache Mahout Machine Learning Library (Clustering)
Spider Machine Learning Library (MATLAB)
Open source clustering software
Nonnegative matrix factorization in Python
OpenSubspace for high-dimensional clustering
Chapters 8 and 9: Outlier Analysis
Scikit Outlier Detection (Python)
Open Source Data Mining Software (WEKA Workbench)
Chapters 10 and 11: Data Classification
Scikit-learn Data Classification and Regression (Python)
Open Source Data Mining Software (WEKA Workbench)
Apache Mahout Machine Learning Library (Classification)
Spider Machine Learning Library (MATLAB)
LibSVM for Support Vector Machines
ENTOOL for Ensemble Learning and Classification
R-archive network with lots of classification and ensemble methods
Chapter 12: Data Streams
IBM Infosphere Streams Platform
Reservoir Sampling Implementation
Sketch and Lossy counting implementations
MADlib sketch and Flajolet-Martin implementation
MOA Toolkit for Massive Online Analytics
Chapter 13: Mining Text Data
GENISM topic modeling in Python
SVMperf Software for scalable text classification
Multinomial Bayes model for classification
Chapter 14: Mining Time-Series Data
Time-series forecasting in R from CRAN
Gait-CAD MATLAB toolbox for clustering, classification, and regression
Cronos open source time-series package
UCR time-series clustering and classification page
Chapter 15: Mining Discrete Sequences
GSP Sequential Pattern Mining from Weka
SPMF Sequential Pattern Mining Implementations
Open Source Data Mining Software (WEKA Workbench)
Sequence mining for computational biology
Chapter 16: Mining Spatial Data
Bunch of spatial and trajectory software
Chapter 17: Mining Graph Data
Ullman algorithm for subgraph isomorphism
Frequent subgraph mining toolbox
Cheminformatics toolbox for kernels
Chapter 18: Mining Web Data
SNAP: Stanford Network Analysis Project
PageRank implementation for very large graphs
Apache Mahout Recommender Systems
Large scale collaborative filtering
Scikit recommender systems in Python
Chapter 19: Social Network Analysis
SNAP: Stanford Network Analysis Project
Weka Package for Semisupervised Learning and Collective Classification
NetKit-SRL for Collective Classification
Link Prediction Method (Lpmade)
Chapter 20: Privacy-Preserving Data Mining
Open source implementation of several anonymization algorithms