The main parts of the course include exploratory Data analysis, frequent pattern
mining, clustering, and classification. The course lays the basic foundations of these
tasks, and it also covers cutting-edge topics such as kernel methods, high-dimensional
Data analysis, and complex graphs and networks. It integrates concepts from related
disciplines such as machine learning and statistics and is also ideal for a course on data
analysis. Most of the prerequisite material is covered in the text, especially on linear
algebra, and probability and statistics.

The course includes many examples to illustrate the main technical concepts. It also
has end-of-chapter exercises, which have been used in class.All of the algorithms in the
course have been implemented by the authors.We suggest that readers use their favorite
Data analysis and mining software to work through our examples and to implement the
algorithms we describe in text; we recommend the R software or the Python language
with its NumPy package.

Having understood the basic principles and algorithms in Data mining and Data analysis, readers will be well equipped to develop their own methods or use more
advanced techniques.

Data Mining
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 course id 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
course include exploratory data analysis, pattern mining, clustering, and
classification. The course 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 course
offers solid guidance in Data mining for participants, researchers, and
practitioners alike.

Key Features:

• Covers both core methods and cutting-edge research

• Algorithmic approach with open-source implementations

• Minimal prerequisites, as all key mathematical concepts are
presented, as is the intuition behind the formulas

• Short, self-contained chapters with class-tested examples and
exercises that allow for flexibility in designing a course and for easy
reference

• Supplementary online resource containing lecture slides, videos,
project ideas, and more

This couse is an outgrowth of data mining courses at Rensselaer Polytechnic Institute
(RPI) and Universidade Federal de Minas Gerais (UFMG); the RPI course has been
offered every Fall since 1998, whereas the UFMG course has been offered since
2002. Although there are several good course on data mining and related topics, we
felt that many of them are either too high-level or too advanced. Our goal was to
write an introductory text that focuses on the fundamental algorithms in data mining
and analysis. It lays the mathematical foundations for the core data mining methods,
with key concepts explained when first encountered; the course also tries to build the
intuition behind the formulas to aid understanding.

• Data Matrix

• Attributes

• Data: Algebraic and Geometric View

• Data: Probabilistic View

• Data Mining

• Univariate Analysis

• Bivariate Analysis

• Multivariate Analysis

• Data Normalization

• Normal Distribution

• Univariate Analysis

• Bivariate Analysis

• Multivariate Analysis

• Distance and Angle

• Discretization

• Graph Concepts

• Topological Attributes

• Centrality Analysis

• Graph Models

• Kernel Matrix

• Vector Kernels

• Basic Kernel Operations in Feature Space

• Kernels for Complex Objects

• High-dimensional Objects

• High-dimensional Volumes

• Hypersphere Inscribed within Hypercube

• Volume of Thin Hypersphere Shell

• Diagonals in Hyperspace

• Density of the Multivariate Normal

• Appendix: Derivation of Hypersphere Volume

• Introduction

• Principal Component Analysis

• Kernel Principal Component Analysis

• Singular Value Decomposition

• Frequent Itemsets and Association Rules

• Itemset Mining Algorithms

• Generating Association Rules

• Maximal and Closed Frequent Itemsets

• Mining Maximal Frequent Itemsets: GenMax Algorithm

• Mining Closed Frequent Itemsets: Charm Algorithm

• Nonderivable Itemsets

• Frequent Sequences

• Mining Frequent Sequences

• SubstringMining via Suffix Trees

• Isomorphism and Support

• Candidate Generation

• The gSpan Algorithm

• Rule and Pattern Assessment Measures

• Significance Testing and Confidence Intervals

• K-means Algorithm

• Kernel K-means

• Expectation-Maximization Clustering

• Preliminaries

• Agglomerative Hierarchical Clustering

• The DBSCAN Algorithm

• Kernel Density Estimation

• Density-based Clustering: DENCLUE

• Graphs and Matrices

• Clustering as Graph Cuts

• Markov Clustering

• External Measures

• Internal Measures

• Relative Measures

• Bayes Classifier

• Naive Bayes Classifier

• K Nearest Neighbors Classifier

• Decision Trees

• Decision Tree Algorithm

• Optimal Linear Discriminant

• Kernel Discriminant Analysis

• Support Vectors and Margins

• SVM: Linear and Separable Case

• Soft Margin SVM: Linear and Nonseparable Case

• Kernel SVM: Nonlinear Case

• SVM Training Algorithms

• Classification Performance Measures

• Classifier Evaluation

• Bias-Variance Decomposition

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