prediction in data mining

8/8/2019 Prediction in Data Mining

1/12

Hong Vit Lm

Nguyn Minh Tn


2/12

Outliney Data Mining Prediction in general

y Definition

y How does Prediction work?y Prediction Evaluation

y Some current research in Data Mining prediction

y Regression

y SVM

y Neural Network


3/12

Definitiony Prediction in Data Mining is a

process to build a continuous-valued function to

predict future val

ued from current data.

y Different with Classification:

y Classification : predict categoricallabels

y Prediction : predict continuous-valued

y Field applied:y Medical , Economic .


4/12

How does Prediction work?

Datapreprocessing

DataCleaning

Relevanceanalysis

Datatransformationsand reduction

Learning Phase

Input:

training set

Output:

Continuous-valuedfunction

Testing Phase

Input:

Testing set

Continuous-valuedfunction

Output:

Accuracy Evaluation

Suitable Predictor

Predictor


5/12

Criteria for comparing prediction

methods

Accuracy

Speed

Robustness

Scalability

Interpretability


6/12

Predictor Error Measuresy Use loss function:

y Absolute error

y

Squared error

y Test error rate:y Mean absolute error

y Mean squared error

y Relative absolute error

y Relative squared error


7/12

Evaluating the Accuracy of

Predictor

y Holdout Methods and Random Sub sampling

y Cross-Validation

y Bootstrap


8/12

Outliney Data Mining Prediction in general

y Definition

y How does Prediction work?y Prediction Evaluation

y Some current research in Data Mining prediction

y Regression

y SVM

y Neural Network


9/12

Regressiony Statistical methodology developed by Sir Frances

Galton

yA good choice when all of the predictor variables arecontinuous valued

y Classification:

y Linear regression /Non-linear regression

y Single variable / Multiple variables


10/12

Support Vector Machines

y SVM : use nonlinear mapping to transform the originaltraining data into a higher dimension then find out thelinear optimal separating hyper plane

y In regression it can be used to learn the input-outputrelationship between input training tuples.

y Pros and Cons:

y Training time low but highlyaccurate


11/12

Neural Network

y Artificial neural networks: Non-linear predictive models thatlearn through training and resemble biological neural networksin structure.

y One of the most commonly used techniques in data mining

y Pros:y Long training timesy Poor interpretability

y Cons:y High tolerance of noisy dataand pattern that have not been trainy

Well-suit for continuous-valued inputs and outputy Success on wide public

y Architecture:y Feed forward

y Algorithm:y Back propagation


12/12

prediction in data mining

Documents