Data Mining A Tutorial Based Primer

Table of Contents

Data Mining Fundamentals

Data Mining: A First View

DATA SCIENCE, ANALYTICS, MINING, AND KNOWLEDGE DISCOVERY IN DATABASES
WHAT CAN COMPUTERS LEARN?
IS DATA MINING APPROPRIATE FOR MY Masalah?
DATA MINING OR KNOWLEDGE ENGINEERING?
A NEAREST NEIGHBOR APPROACH
DATA MINING, BIG DATA, AND CLOUD COMPUTING
DATA MINING ETHICS
INTRINSIC VALUE AND CUSTOMER CHURN
CHAPTER SUMMARY
KEY TERMS

Data Mining: A Closer Look
DATA MINING STRATEGIES
SUPERVISED DATA MINING TECHNIQUES

ASSOCIATION RULES

CLUSTERING TECHNIQUES

EVALUATING PERFORMANCE
CHAPTER SUMMARY

KEY TERMS

Basic Data Mining Techniques
CHAPTER OBJECTIVES

DECISION TREES
A BASIC COVERING RULE ALGORITHM

GENERATING ASSOCIATION RULES

THE
K-MEANS ALGORITHM
GENETIC LEARNING

CHOOSING A DATA MINING TECHNIQUE

CHAPTER SUMMARY

KEY TERMS

Tools for Knowledge Discovery

Weka—An Environment for Knowledge Discovery

GETTING STARTED WITH WEKA

BUILDING DECISION TREES

GENERATING PRODUCTION RULES WITH PART

ATTRIBUTE SELECTION AND NEAREST NEIGHBOR CLASSIFICATION

ASSOCIATION RULES

COST/BENEFIT ANALYSIS

UNSUPERVISED CLUSTERING WITH THE
K-MEANS ALGORITHM

CHAPTER SUMMARY

Knowledge Discovery with RapidMiner

GETTING STARTED WITH RAPIDMINER

BUILDING DECISION TREES

GENERATING RULES

ASSOCIATION RULE LEARNING

UNSUPERVISED CLUSTERING WITH
K-MEANS

ATTRIBUTE SELECTION AND NEAREST NEIGHBOR CLASSIFICATION

CHAPTER SUMMARY

The Knowledge Discovery Process

A PROCESS Model FOR KNOWLEDGE DISCOVERY

GOAL IDENTIFICATION 2022.3 CREATING A Bulan-bulanan DATA SET
DATA PREPROCESSING

DATA TRANSFORMATION
DATA MINING
INTERPRETATION AND EVALUATION
TAKING ACTION

THE CRISP-DM PROCESS Lengkap

CHAPTER SUMMARY

KEY TERMS

Formal Evaluation Techniques

WHAT SHOULD BE EVALUATED?
TOOLS FOR EVALUATION
COMPUTING TEST SET CONFIDENCE INTERVALS

COMPARING SUPERVISED LEARNER MODELS

UNSUPERVISED EVALUATION TECHNIQUES

EVALUATING SUPERVISED MODELS WITH NUMERIC OUTPUT

COMPARING MODELS WITH RAPIDMINER

ATTRIBUTE EVALUATION FOR MIXED DATA TYPES

PARETO LIFT CHARTS

CHAPTER SUMMARY

KEY TERMS

Building Neural Networks

Neural Networks

FEED-FORWARD NEURAL NETWORKS

NEURAL NETWORK TRAINING: A CONCEPTUAL VIEW

NEURAL NETWORK EXPLANATION

GENERAL CONSIDERATIONS

NEURAL NETWORK TRAINING: A DETAILED VIEW
CHAPTER SUMMARY
KEY TERMS

Building Neural Networks with Weka

DATA SETS FOR BACKPROPAGATION LEARNING

MODELING THE EXCLUSIVE-OR FUNCTION: NUMERIC OUTPUT

MODELING THE EXCLUSIVE-OR FUNCTION: CATEGORICAL OUTPUT

MINING SATELLITE IMAGE DATA
UNSUPERVISED NEURAL Ambai CLUSTERING
CHAPTER SUMMARY

KEY TERMS

Building Neural Networks with RapidMiner
MODELING THE EXCLUSIVE-OR FUNCTION

MINING SATELLITE IMAGE DATA

PREDICTING CUSTOMER CHURN

RAPIDMINER’S SELF-ORGANIZING MAP OPERATOR

CHAPTER SUMMARY

Advanced Data Mining Techniques

Supervised Statistical Techniques

BAYES CLASSIFIER

SUPPORT VECTOR MACHINES

LINEAR REGRESSION ANALYSIS

REGRESSION TREES

LOGISTIC REGRESSION

CHAPTER SUMMARY

KEY TERMS

Unsupervised Clustering Techniques

AGGLOMERATIVE CLUSTERING

CONCEPTUAL CLUSTERING

EXPECTATION MAXIMIZATION

GENETIC ALGORITHMS AND UNSUPERVISED CLUSTERING
CHAPTER SUMMARY

KEY TERMS

Specialized Techniques
TIME-SERIES ANALYSIS

MINING THE WEB

MINING TEXTUAL DATA

TECHNIQUES FOR LARGE-SIZED, IMBALANCED, AND STREAMING DATA
ENSEMBLE TECHNIQUES FOR IMPROVING PERFORMANCE

CHAPTER SUMMARY

KEY TERMS

The Data Warehouse
OPERATIONAL DATABASES

DATA WAREHOUSE DESIGN

ONLINE ANALYTICAL PROCESSING

EXCEL PIVOT TABLES FOR DATA ANALYTICS
CHAPTER SUMMARY

KEY TERMS

Source: https://www.routledge.com/Data-Mining-A-Tutorial-Based-Primer-Second-Edition/Roiger/p/book/9781498763974