Introduction to Data Mining-reading notes (1)-Overview | Catalogue [2016-8-8]

This book provides a comprehensive overview of data mining, covering five topics: data, classification, correlation analysis, clustering, and anomaly detection. In addition to anomaly detection, each topic has two chapters. The previous chapter covers basic concepts, representative algorithms, and evaluation techniques, and the latter chapter discusses advanced concepts and algorithms. This allows for a thorough understanding of the basis of data mining, as well as the ability to learn more important advanced topics.

Directory

The 1th Chapter Introduction

1.1 What is Data mining

1.2 Problems to be solved in data mining

1.3 Origins of data mining

1.4 Data Mining tasks

1.5 contents and organization of the book

Chapter 2nd Data

　　2.1 Data types

2.2 Data quality

2.3 Data preprocessing

2.4 Measurement of similarity and dissimilarity

The 3rd chapter explores the data

　　3.1 Iris Data Set

3.2 Summary Statistics

3.3 Visualization

3.4 OLAP and multi-data analysis

4th classification: Basic concepts, decision trees and models

　　4.1 Preliminary knowledge

4.2 General methods for solving classification problems

4.3 Decision tree Induction

4.4 Overfitting of the model

4.5 Evaluating the performance of classifiers

Over-fitting in decision tree induction of 4.6 processing

5th. Category: Other technologies

　　5.1 Rule-based classifiers

5.2 Nearest Neighbor Classifier

5.3 Bayesian classifier

5.4 Artificial Neural network

5.5 Support Vector Machine

5.6 Combination Method

5.7 Unbalanced class Problems

6th. Correlation Analysis: Basic concepts and algorithms

　　6.1 Problem definition

6.2 Generation of frequent itemsets

6.3 Rule Generation

6.4 Compact representation of frequent itemsets

6.5 Other ways to generate frequent itemsets

6.6 Evaluation of correlation patterns

6.7 Influence of the tilt support degree distribution

7th. Correlation Analysis: Advanced Concepts

　　7.1 Handling Classification Properties

7.2 Working with continuous properties

7.3 Dealing with Conceptual layering

7.4 Sequence Mode

7.5 Sub-graph mode

7.6 Non-frequent mode

The 8th Chapter Clustering Analysis: Basic concepts and algorithms

　　8.1 Overview

8.2 k mean value

8.3 Condensed Hierarchical clustering

8.4 DBSCAN

8.5 Cluster evaluation

The 9th Chapter Clustering Analysis: Other problems and algorithms

　　9.1 Characteristics of data, clusters, and clustering algorithms

9.2 Prototype-based clustering

9.3 Density-based clustering

9.4 Graph-based clustering

9.5 Scalable Clustering algorithm

10th Chapter Anomaly Detection

　　10.1 Preliminary knowledge

10.2 Statistical methods

10.3 Outlier detection based on proximity

10.4 Density-based outlier detection

10.4 Clustering-based technology

Appendix A linear algebra

Appendix B-Dimensional attribution

Appendix C Probability Statistics

Appendix D Regression

Appendix E Optimization

Introduction to Data Mining-reading notes (1)-Overview | Catalogue [2016-8-8]