Modeling Data the different views on Data Mining.

Modeling Data

the different views on Data Mining

Views on Data Mining

Fitting the data Density Estimation Learning

being able to perform a task more accurately than before Prediction

use the data to predict future data Compressing the data

capture the essence of the data discard the noise and details

Data fitting

Very old concept Capture function between variables Often

few variables simple models

Functions step-functions linear quadratic

Trade-off between complexity of model and fit (generalization)

responseto newdrug

body weight

responseto newdrug

body weight

moneyspent

income

¾ ratio

Kleiber’s Law of Metabolic Rate

Density Estimation

Dataset describes a sample from a distribution Describe distribution is simple terms

prototypes

Density Estimation

Other methods also take into account the spatial relationships between prototypes

Self-Organizing Map (SOM)

Learning

Perform a task more accurately than before Learn to perform a task (at all)

Suggests an interaction between model and domain perform some action in domain observe performance update model to reflect desirability of action

Often includes some form of experimentation

Not so common in Data Mining often static data (warehouse), observational data

Prediction: learning a decision boundary

Compression

Compression is possible when data contains structure (repeting patterns)

Compression algorithms will discover structure and replace that by short code

Code table forms interesting set of patterns

A B C D E F

1 0 1 1 0 0

1 1 1 1 1 0

0 1 0 1 1 0

•Pattern ACD appears frequently

•ACD helps to compress the data

•ACD is a relevant pattern to report

Compression

Paul Vitanyi (CWI, Amsterdam) Software to unzip identity of unknown composers

Beethoven, Miles Davis, Jimmy Hendrix

SARS virus similarity

internet worms, viruses intruder attack traffic images, video, …

Mobile calls: modeling duration of calls

More data: linear model

Even more data: still linear?

Modeling Data the different views on Data Mining.

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