Mining of Massive Data Sets 2nd Edition by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman ISBN 1107077230 9781107077232
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Authors:Jure Leskovec; Anand Rajaraman; Jeffrey David Ullman , Series:IT & Computer [216] , Tags:Business & Economics; Knowledge Capital; Computers; Artificial Intelligence; General; Computer Vision & Pattern Recognition; Database Administration & Management; Data Science; Data Analytics; Optical Data Processing; Language Arts & Disciplines; Library & Information Science , Author sort:Leskovec, Jure & Rajaraman, Anand & Ullman, Jeffrey David , Ids:Google; 9781108476348 , Languages:Languages:eng , Published:Published:Jan 2020 , Publisher:CAMBRIDGE UNIVERSITY PRESS , Comments:Comments:”The Web, social media, mobile activity, sensors, Internet commerce, and many other modern applications provide many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be used on even the largest datasets. It begins with a discussion of the MapReduce framework and related techniques for efficient parallel programming. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widely known, is essential when seeking similar objects in a very large collection without having to compare each pair of objects. Stream-processing algorithms for mining data that arrives too fast for exhaustive processing are also explained. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering, each from the point of view that the data is too large to fit in main memory. Two applications: recommendation systems and Web advertising, each vital in e-commerce, are treated in detail. Later chapters cover algorithms for analyzing social-network graphs, compressing large-scale data, and machine learning. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike”–
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ISBN 10: 1107077230
ISBN 13: 9781107077232
Author: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
Mining of Massive Data Sets 2nd Edition Table of contents:
Chapter 1: Introduction
- What is Data Mining?
- Types of Data Sets and Mining Problems
- Overview of Mining Techniques
- Applications of Data Mining
Chapter 2: MapReduce and Distributed Computing
- Basics of Parallel Computing
- MapReduce Framework
- Distributed Data Storage
- Implementing MapReduce for Data Mining Tasks
Chapter 3: Mining Frequent Patterns
- Association Rule Mining
- Apriori Algorithm
- Finding Frequent Itemsets
- Mining Frequent Subgraphs
- Generalized Association Rules
Chapter 4: Clustering
- Basics of Clustering
- K-Means Algorithm
- Hierarchical Clustering
- Density-Based Clustering
- Evaluating Clustering Results
Chapter 5: Classification
- Introduction to Classification Problems
- Decision Trees and Random Forests
- Support Vector Machines
- K-Nearest Neighbors
- Performance Metrics for Classification
Chapter 6: Mining Graphs and Networks
- Graph Mining Overview
- Representing Graphs
- Social Network Analysis
- Algorithms for Graph Mining
- Community Detection
Chapter 7: Matrix Factorization and Collaborative Filtering
- Introduction to Matrix Factorization
- Singular Value Decomposition (SVD)
- Collaborative Filtering in Recommender Systems
- Applications of Collaborative Filtering
Chapter 8: Large-Scale Machine Learning
- Scaling Algorithms for Large Data Sets
- Parallelizing Machine Learning Algorithms
- Stochastic Gradient Descent
- Online Learning Algorithms
Chapter 9: Mining Stream Data
- Data Streams and Challenges
- Stream Data Models
- Online Algorithms for Mining Stream Data
- Applications of Stream Mining
Chapter 10: Advanced Topics
- Dimensionality Reduction
- Anomaly Detection
- Privacy-Preserving Data Mining
- Ethics in Data Mining
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