Download What is data mining?

運用資料探勘視覺化技術
建置旅遊服務系統
Construction of Travel Service System
Using Visualization Technology of Data
Mining
Presenter: Hui-Yu Huang, Ph.D
E-mail: [email protected]
Date: 2004/3/2
Outline
What is data mining?
What is visualization of data mining?
Introduction
Proposed approach
Experimental results
Conclusions
2
Outline
What is data mining?
What is visualization of data mining?
Introduction
Proposed approach
Experimental results
Conclusions
3
What is Data Mining? (1/2)
Introduction
Technologies
- Classification
- Clustering
- Association rule
- Time-series analysis and sequence
discovery
4
What is Data Mining? (2/2)
Applications
- Customer profiling
- Targeted marketing
- Market-basket analysis
Others
- Multimedia (Text,Video,Audio) mining
- Web Mining
5
Outline
What is data mining?
What is visualization of data
mining?
Introduction
Proposed approach
Experimental results
Conclusions
6
Visualization of Data Mining
Introduction
Classification of visual data mining
techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
7
Introduction (1/2)
Overview
Benefits of visual data exploration
- easily deal with highly inhomogeneous
and noisy data.
- intuitive and no need to understand
the complex mathematical or statistical
algorithms.
8
Introduction (2/2)
Visual exploration paradigm
- three step processes:
Overview First
Zoom and Filter
Details-On-Demand
9
Visualization of Data Mining (cont.)
Introduction
Classification techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
10
Classification Techniques
11
One-Dimensional Data
ThemeRiver
12
Two-Dimensional Data
Polaris
MGV
13
http://www.e2di.com/
14
Multidimensional Data
Polaris
15
Hierarchies and Graphs
MGV
16
Algorithms and Software
Polaris
17
Standard 2D/3D Displays
18
Icon-Based Displays(1/3)
19
Icon-Based Displays(2/3)
20
Icon-Based Displays(3/3)
21
Dense Pixel Displays
22
Stacked Displays
深
度
區域
23
Visual Data Mining (cont.)
Introduction
Classification techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
24
Data Type to be Visualized
One-dimensional data--temporal data
Two-dimensional data--geographical data
Multidimensional data--tables from relational
databases
Text and hypertext--multimedia web page
Hierarchies and graphs--e-mail
interrelationships
Algorithms and software—visualizing errors
25
Visual Data Mining (cont.)
Introduction
Classification of visual data mining
techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
26
Visualization Techniques
Iconic displays
Dense pixel displays
Stacked displays
27
Visual Data Mining (cont.)
Introduction
Classification techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
28
Distortion Techniques
Dynamic projections--2D show
multidimensional
Interactive filtering
Interactive zooming
Interactive distortion
Interactive linking
29
Interactive Filtering
Browsing
Querying
Magic Lenses
Interesting Subset
Large Data Sets
30
Interactive Zooming
31
Interactive Distortion
Drill-Down
32
Interactive Linking
Hot Point
33
Interactive Linking (cont.)
34
Visual Data Mining (cont.)
Introduction
Classification techniques
Data type to be visualized
Visualization techniques
Distortion techniques
Conclusions
35
Conclusions
Use information visualization technology
for an improved data analysis
- Potential and many applications
Involve the tight integration of
visualization techniques
-Statistics, machine learning, simulation
The ultimate goal
36
References
[1] L. Nowell, S. Havre, B. Hetzler, and P. Whitney,
Themeriver:Visualizing Thematic Changes in Large Document
Collections, IEEE Trans. Visualization and Computer Graphics,
vol. 8, no. 1, pp. 9-20, Jan.-Mar., 2002.
[2] D. Tang, C. Stolte, and P. Hanrahan, ?Polaris: A System for
Query,Analysis and Visualization of Multidimensional Relational
Databases, IEEE Trans. Visualization and Computer Graphics,
vol. 8, no.1, pp. 52-65, Jan.-Mar., 2002.
[3] J. Abello and J. Korn, ?MGV: A System for Visualizing Massive
Multidigraphs, IEEE Trans. Visualization and Computer
Graphics, vol. 8, no. 1, pp. 21-38, Jan.-Mar., 2002.
37
[4] N. Lopez, M. Kreuseler, and H. Schumann, ?A Scalable
Framework for Information Visualization,o IEEE Trans.
Visualization and Computer Graphics, vol. 8, no. 1, pp. 3951, Jan.-Mar., 2002.
[5] D. Keim, ?Designing Pixel-Oriented Visualization
Techniques:Theory and Applications,o IEEE Trans.
Visualization and Computer Graphics, vol. 6, no. 1, pp. 5978, Jan.-Mar., 2000.
[6] Information Visualization in Data Mining and
Knowledge Discovery. Edited by Usama Fayyad Geo Rges G.
Grinstein Andreas Wierse, Morgan Kaufmann Publishers
2002.
38
39
40
Outline
What is data mining?
What is visualization of data mining?
Introduction
Proposed approach
Experimental results
Conclusions
41
Introduction(1/2)
Overview
-Using human
Unique thinking creative abilities
Vague feature recognition and cognitive
ability for the complicated pictures.
-Find the visual pattern
-Application on travel service
42
Introduction(2/2)
Objective
- Dataset features and relationships
- Prediction
- Estimation
- Association
- Clustering
43
Outline
What is data mining?
What is visualization of data mining?
Introduction
Proposed approach
Experimental results
Conclusions
44
Proposed Approach
Taiwan travel service information
system’s (TTSIS) framework
Data mining techniques
Visualization techniques
Advantages
45
Proposed Approach
Taiwan travel service information
system’s (TTSIS) framework
Data mining techniques
Visualization techniques
Advantages
46
Taiwan Travel Service Information System’s
(TTSIS) Framework (1/5)
47
Taiwan Travel Service Information System’s
(TTSIS) Framework (2/5)
Data set
- Travel information from the web, public
database or queried test
Data set application program interface
- Microsoft visual basic. NET
- SQL server 2000
48
Taiwan Travel Service Information System’s
(TTSIS) Framework (3/5)
Data mining process
- Associative technique
- Clustering
- Time-series analysis and sequence
discovery
Visualization process
- 2-dimensional graph
49
Taiwan Travel Service Information System’s
(TTSIS) Framework (4/5)
Multimedia geography information system
Decision knowledge database
50
Taiwan Travel Service Information System’s
(TTSIS) Framework (5/5)
User interface API
-Microsoft visual basic. NET
51
Proposed Approach
Taiwan travel service information
system’s (TTSIS) framework
Data mining techniques
Visualization techniques
Advantages
52
Data Mining Techniques
Association rule
- Frequent pattern tree method
Clustering
- K-mean method
Time-series analysis and sequence
discovery
- Using time hierarchical
53
Proposed Approach
Taiwan travel service information
system’s (TTSIS) framework
Data mining techniques
Visualization techniques
Advantages
54
Visualization Techniques
Queried test (Travel Service Satisfied )
- Iconic representation
Clustering
- Using Java language 2D/3D graph tool
to show data mining results
55
Proposed Approach
Taiwan travel service information
system’s (TTSIS) framework
Data mining techniques
Visualization techniques
Advantages
56
Advantages
Construct the complete system
- TTSIS
Visualization of the result of the
data mining
Interaction relation of travel service
industry and consumers
57
Outline
What is data mining?
What is visualization of data mining?
Introduction
Proposed approach
Experimental results
Conclusions
58
Experimental Results (1/4)
Experimental process and results
-
Data source: randomly simple inquiry
from e-mail and web network
59
Experimental Results (2/4)
12 345
60
Experimental Results (3/4)
Consumption
behavior
Life
behavior
Satisfaction degree of quality
of service and travel customer
61
Experimental Results (3/3)
Problem
- The data is difficult to globally obtain.
Discussions
- Interactive visual data mining
- Decision analysis role
- Flexibility
62
Outline
What is data mining?
What is visual data mining?
Introduction
Proposed approach
Experimental results
Conclusions
63
Conclusions
Quickly integrate visual data-mining
environment
Integrate visualization and data mining
Techniques
Integrate multimedia geography
information system and decision
knowledge database
Design more friendly User Interface
64
The End
Thanks for
yours attendance
65