santiago gonzález tortosa data mining vs visualization

17
Santiago González Tortosa <[email protected]> Data Mining VS Visualization

Upload: dortha-hines

Post on 20-Jan-2016

214 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Santiago González Tortosa Data Mining VS Visualization

Santiago González Tortosa<[email protected]>

Data Mining VS

Visualization

Page 2: Santiago González Tortosa Data Mining VS Visualization

I. Data Mining VS VisualizationII. Visualize to DMIII.DM to Visualize (to DM)IV.Real world work:

I. Global Behavior Modeling: A New approach to Grid autonomic management

Contents

2

Page 3: Santiago González Tortosa Data Mining VS Visualization

• Data Mining – Knowledge discovery and extration– Not always is easy to see patterns,

distributions, etc.

• Visualization– Represents data (2D, 3D, Virtual Reality,…)– Helps to extract patterns– Not always is easy to represent data in 2

or 3 dimensions

Data Mining VS Visualization

3

Page 4: Santiago González Tortosa Data Mining VS Visualization

• Visualization help us to extract any pattern in the data

4

Visualize to DM

Page 5: Santiago González Tortosa Data Mining VS Visualization

• Visualization help us to extract any pattern in the data

5

Visualize to DM

Page 6: Santiago González Tortosa Data Mining VS Visualization

• Data contains N (> 3) features– Curse of Dimensionality

• We want to visualize all data• Dimensionality Reduction

– Reduce number of features– Transform and create new features

6

DM to Visualize

Page 7: Santiago González Tortosa Data Mining VS Visualization

• Dimensionality Reduction– L.J.P. van der Maaten, E.O. Postma, and H.J. van

den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009

• Convex techniques: optimize an objective function that does not contain any local optima

• Nonconvex techniques: optimize objective functions that do contain local optima

7

DM to Visualize

Page 8: Santiago González Tortosa Data Mining VS Visualization

• Optimization techniques (hill climbing, evolutive, etc.)

8

DM to Visualize

Page 9: Santiago González Tortosa Data Mining VS Visualization

• Optimization techniques– One objective– One objective with constraints (Semi-

Supervised and labeling)– Multiobjective

9

DM to Visualize

Page 10: Santiago González Tortosa Data Mining VS Visualization

• Example: Optimize axis

10

DM to Visualize

Page 11: Santiago González Tortosa Data Mining VS Visualization

• Dimensionality Reduction in 2 phases:– FSS: Feature Subset Selection (wrapper,

needed CLASS!)– Transformation and creation of new features

(f.e. PCA)

11

DM to Visualize

Page 12: Santiago González Tortosa Data Mining VS Visualization

• Example of Dimensionality Reduction in 2 phases– User expert interacts

12

DM to Visualize

Page 13: Santiago González Tortosa Data Mining VS Visualization

• DM to Visualize….to DM!!• The idea is to obtain new knowledge or

patterns viewing the data.– Supervised info: data with the same class are

represented in the same area (KNN).– Unsupervised info: data is agrouped

13

DM to Visualize

Page 14: Santiago González Tortosa Data Mining VS Visualization

• Example that some data is agrouped

14

DM to Visualize

Page 15: Santiago González Tortosa Data Mining VS Visualization

• Visualization– 2D and 3D visualization– Virtual Reality

• Inmersion• Interaction• Imagination

– Augmented Reality

15

DM to Visualize

Page 16: Santiago González Tortosa Data Mining VS Visualization

Global Behavior Modeling: A New approach to Grid

autonomic management

Jesus Montes <[email protected]>

16

Real world work

Page 17: Santiago González Tortosa Data Mining VS Visualization

Santiago González Tortosa<[email protected]>

Data Mining VS

Visualization