Visual Analytics and Data Mining Visual Analytics and Data Mining - - PDF document

▶
visual analytics and data mining visual analytics and
SMART_READER_LITE
LIVE PREVIEW

Visual Analytics and Data Mining Visual Analytics and Data Mining - - PDF document

Mar 23, 2023 205 likes •315 views

Visual Analytics and Data Mining Visual Analytics and Data Mining in S- in S -T T- -applications applications Gennady Andrienko & Natalia Andrienko Fraunhofer Institute AIS Sankt Augustin Germany http://www.ais.fraunhofer.de/and

slide-1
SLIDE 1

1

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 1

Visual Analytics and Data Mining Visual Analytics and Data Mining in S in S-

  • T

T-

  • applications

applications

Gennady Andrienko & Natalia Andrienko Fraunhofer Institute AIS Sankt Augustin Germany http://www.ais.fraunhofer.de/and

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 2

A View on S A View on S-

  • T Data Mining

T Data Mining

Method(s) Input data Output data

May have many parameters; May be computationally intensive Need to be interpreted; To be used for directing further analysis Complex and multidimensional; May contain errors Data Complexities: 1) Space, 2) Time, 3) Multiple attributes & dimensions 4) Outliers, discontinuities

slide-2
SLIDE 2

2

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 3

Complexities Complexities

  • Number of attributes
  • Length of time series
  • Number of spatial objects
  • High dimensionality
  • Abrupt temporal changes
  • Great variability of values

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 4

Complexities: example 1 Complexities: example 1

slide-3
SLIDE 3

3

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 5

Complexities: example 2 Complexities: example 2

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 6

Aggregation method 1: by intervals Aggregation method 1: by intervals

slide-4
SLIDE 4

4

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 7

Aggregation method 2: by Aggregation method 2: by quantiles quantiles

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 8

Attend to particulars: high variation Attend to particulars: high variation

1. Aggregate time graph by quantiles 2. Save counts 3. Visualise e.g. on a scatter plot 4. Select items with high variation

slide-5
SLIDE 5

5

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 9

Attend to particulars: high fluctuation Attend to particulars: high fluctuation

  • Select items with maximal

number of jumps between quantiles

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 10

Attend to particulars: stable extremes Attend to particulars: stable extremes

  • Select items being always in the

topmost 10%

slide-6
SLIDE 6

6

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 11

Attend to particulars: extreme changes Attend to particulars: extreme changes

1. Transform the time graph to show changes 2. Select extreme changes in a specific year (here 2003)

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 12

Visual Analytics in Data Mining Visual Analytics in Data Mining

Data preview

visualisation, display manipulation, etc.

Method selection Data preparation

data manipulation

Method application Result exploration and interpretation

visualisation, etc.

slide-7
SLIDE 7

7

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 13

Requirements to Visual Analytics Requirements to Visual Analytics

  • Space- and Time-awareness
  • Work with complex multidimensional data
  • Support for uncertain and missing data
  • Scalability
  • Support and encouraging of several

complementary views on the same data

  • Dynamic linking and coordination of several data

displays

  • From the overall view to particulars of interest
  • From idea generation to hypothesis testing using

statistical methods, followed by reporting

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 14

Potentially useful tools for MSTD Potentially useful tools for MSTD

Information visualisation tools, for example, HCE & TimeSearcher from HCIL, Univ. Maryland Geovisualisation tools, for example GeoVistaStudio (Penn State Univ.) and Descartes/CommonGIS (Fraunhofer Institute AIS) Graphical statistics tools, for example, Manet & Mondrian (Augsburg Univ.)

Usually such systems are research prototypes that implement innovative ideas, but provide restricted functionality and limited user support

slide-8
SLIDE 8

8

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 15

Still open issues (for all tools!) Still open issues (for all tools!)

Work with qualitative (non-numeric) data Work with fuzzy, uncertain, and missing data Continue scalability efforts Support in processing and management of findings: recording, structuring, browsing, searching, checking, combining, interpreting… Help in visual communication of derived data, constructed knowledge, and recommended decisions Adaptability to user, data, tasks, and hardware Embedding intelligence into software for helping users and avoiding cognitive overload

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 16

EDA: from Practice to Practical Theory EDA: from Practice to Practical Theory

Data Tasks Tools Principles

to appear ≈ end 2005

slide-9
SLIDE 9

9

Mining Spatio-Temporal Data @ PKDD, Porto, 2.10.2005 17

Visual Analytics & Data Mining Visual Analytics & Data Mining

  • 1. Do they need each other?
  • 2. How to benefit from combining two

scientific disciplines and related technologies?

  • 3. How to develop each of two scientific

disciplines for achieving a synergy?