Data Mining and Analysis Fundamental Concepts and Algorithms 1st Edition by Mohammed Zaki
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Authors:Mohammed J. Zaki; Wagner Meira, Jr , Series:IT & Computer [231] , Tags:Computers; Computer Science; Database Administration & Management; Data Science; Data Analytics; Optical Data Processing; Foundations and Algorithms , Author sort:Zaki, Mohammed J. & Wagner Meira, Jr , Ids:Google; 9780521766333 , Languages:Languages:eng , Published:Published:May 2014 , Publisher:CAMBRIDGE UNIVERSITY PRESS , Comments:Comments:The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses 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. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike. Key features: • Covers both core methods and cutting-edge research • Algorithmic approach with open-source implementations • Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas • Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference • Supplementary website with lecture slides, videos, project ideas, and more
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Author: Mohammed Zaki
Data Mining and Analysis Fundamental Concepts and Algorithms 1st Edition Table of contents:
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Introduction to Data Mining and Analysis
- Overview of Data Mining
- The Data Mining Process
- Applications of Data Mining
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Data Preprocessing
- Data Cleaning
- Data Transformation and Reduction
- Data Discretization and Binarization
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Similarity and Distance Metrics
- Measuring Similarity and Dissimilarity
- Applications in Data Mining
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Classification
- Decision Trees
- Naïve Bayes Classifier
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Ensemble Methods
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Clustering
- Hierarchical Clustering
- Partitional Clustering (k-Means, k-Medoids)
- Density-Based Clustering (DBSCAN)
- Evaluation of Clustering Results
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Association Rule Mining
- Frequent Itemset Generation
- Apriori Algorithm
- FP-Growth Algorithm
- Applications of Association Rules
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Anomaly Detection
- Techniques for Anomaly Detection
- Statistical Approaches
- Proximity-Based and Density-Based Methods
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Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Applications in Visualization and Data Compression
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Graph Mining
- Introduction to Graphs
- Mining Frequent Subgraphs
- Applications in Social Networks
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Text Mining
- Text Preprocessing and Representation
- Information Retrieval
- Sentiment Analysis and Topic Modeling
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Web and Multimedia Mining
- Mining Web Content, Structure, and Usage
- Multimedia Data Mining
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Data Mining Algorithms
- Parallel and Distributed Data Mining
- Scalability and Efficiency Considerations
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Case Studies and Applications
- Data Mining in Healthcare, Finance, and Retail
- Future Trends in Data Mining
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Conclusion and Future Directions
- Emerging Research Areas
- Ethical and Social Implications
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