Data Mining: Exploring Data
Lecture Notes for Chapter 3
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
8/05/2005
1
What is data exploration?
A p
Data Mining
Anomaly Detection
Lecture Notes for Chapter 10
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Anomaly/Outlier Detection
Data Mining
Cluster Analysis: Advanced Concepts
and Algorithms
Lecture Notes for Chapter 9
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2
Data Mining
Cluster Analysis: Basic Concepts
and Algorithms
Lecture Notes for Chapter 8
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
Data Mining
Association Rules: Advanced Concepts
and Algorithms
Lecture Notes for Chapter 7
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/
Data Mining
Association Analysis: Basic Concepts
and Algorithms
Lecture Notes for Chapter 6
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/
Data Mining
Classification: Alternative Techniques
Lecture Notes for Chapter 5
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Rule-B
Data Mining
Classification: Basic Concepts, Decision
Trees, and Model Evaluation
Lecture Notes for Chapter 4
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to
Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
What is Data?
Collection of data object
Data Mining: Introduction
Lecture Notes for Chapter 1
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Why Mine Data? Commercial Viewp
4
Classication:
Basic Concepts,
Decision Trees, and
Model Evaluation
Classication, which is the task of assigning objects to one of several predened
categories, is a pervasive problem that encompasses
TURBMW06_013234761X.QXD
3/7/07
8:07 PM
Page 1
ONLINE CHAPTER
Neural Networks 6 for Data Mining
Learning Objectives Understand the concept and different types of artificial neural networks (ANN) Learn
Data Mining
Case 1 : Improving
Direct Mail Responses
Mellon Bank Corporation is a major financial
services company headquartered in Pittsburgh,
Pennsylvania. Its two core businesses are
investment se
Data Mining
C. Decision Tree Models
ID3 (by Quinlan in 1979)
C4.5 (by Quinlan in 1993)
CART (by Breiman 1984)
First term, 11/12
CSCI5180 Lecture Slides, Laiwan Chan,
CUHK
57
Data Mining
Iterative D
Data Mining
Decision Tree
First term, 11/12
CSCI5180 Lecture Slides, Laiwan Chan,
CUHK
1
Data Mining
Outline
A. Introduction to Decision Tree ?
B. Measurement of impurity
C. Decision Tree Models and t
Data Mining
First term, 11/12
Tree Pruning
CSC5180 Lecture Slides, written by
Laiwan Chan, CUHK
1
Data Mining
Overfit
A decision tree, d, is said to overfit the
training data if there exists some tre
Data Mining
Neural Networks
First term, 11/12
CSCI5180 Lecture Slides, Laiwan Chan, CUHK
1
Data Mining
Outline
(Chapter 5.4)
A. What is a neural network ?
B. Model and operation of a neuron
C. Single
Data Mining
Customer
Ranking Model
Objective :
In a business with a large customer base, we
want to rank the existing customer set based
on a set of parameters that defines what a
good customer means
Data Mining
Model Evaluation
Metrics for Performance Evaluation (Chapter 5.7)
How to evaluate the performance of a model?
Methods for Performance Evaluation (Chapter 4.5)
How to obtain reliable es
Data Mining
Linear Discriminant
Classifiers
The discriminant functions are linear in the
components of x or linear in some given set
of functions of x.
g(x) = WT x + Wo
where W = weight vector,
Wo =
Data Mining
Classification
First term, 11/12
CSCI5180 Lecture Slides, Laiwan Chan,
CUHK
1
Data Mining
Classification
Definition :
Classification is the process of assigning
classes, or categories, to
Data Mining
Market Segmentation
An example appeared in Chapter 9 of Data Mining
with Neural Networks by Joseph P. Bigus.
Objective : To understand the customers, to find
out the typical customers an
Mutual Fund Selection
Data Mining
A mutual fund is a form of collective investment that
pools money from many investors and invests their money
with a predetermined investment objective. The fund
man
Data Mining
4.3. Hierarchical Clustering
Methods
The partition of data is not done at a
single step.
Produces a set of nested clusters
organized as a hierarchical tree
First term, 11/12
CSCI5180 Lec
Data Mining
3. Types of Clusters
Well-separated clusters
Center-based clusters
Graph-Based clusters
Density-based clusters
Property or Conceptual
Described by an Objective Function
First term, 1
Data Mining
Clustering
First term, 11/12
CSCI5180 Lecture Slides, Laiwan Chan, CUHK
1
Data Mining
Summary
1. Introduction
2. Types of Data and Similarity Measurements
- criteria used in clustering (Ch