Nov 30 2010 Give an example of a problem that might benefit from feature creation How does DENCLUE form clusters Why does DENCLUE use gridcells What are the main differences between DENCLUE and DBSCAN ID: 483912
Download Presentation The PPT/PDF document "Questions and Topics Review" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Questions and Topics Review Nov. 30, 2010
Give
an example of a problem that might benefit from feature creation
How does DENCLUE form clusters? Why does DENCLUE use grid-cells? What are the main differences between DENCLUE and DBSCAN?
Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (
0,1
), (2,2)}
{(
3,2), (3,3)}.
Compare Decision Trees, Support Vector Machines, and K-NN with respect to the number of decision boundary each approach uses!
K-NN
is a lazy approach; what does it mean? What are the disadvantages of K-NN’s lazy approach? Do you see any advantages in using K-NN’s lazy approach.
Why do some support vector machine approaches map examples from a lower dimensional space to a higher dimensional space?
What
is the role of slack variables in the Linear/SVM/Non-separable approach (textbook pages 266-270)—what do they measure? What properties of
hyperplanes
are maximized by the objective function f(w) (on page 268) in the approach?
Silhouette:
For an individual point,
i
Calculate
a
= average distance of
i
to the points in its cluster
Calculate
b
= min (average distance of
i
to points in another cluster)
The silhouette coefficient for a point is then given by:
s = (b-a)/max(
a,b
) Slide2
Support Vector Machines
What if the problem is not linearly separable?Slide3
Linear SVM for Non-linearly Separable Problems
What if the problem is not linearly separable?
Introduce slack variables
Need to minimize:
Subject to (i=1,..,N):
C is chosen using a validation set trying to keep the margins wide while keeping the training error low.
Measures testing error
Inverse size of margin
between hyperplanes
Parameter
Slack variable
allows constraint violation
to a certain degree Slide4
Questions and Topics Review Nov. 30, 2010
Discussion of Problem1/2of Assignment4
Give
an example of a problem that might benefit from feature creation
How does DENCLUE form clusters? Why does DENCLUE use grid-cells? What are the main differences between DENCLUE and DBSCAN?
Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (0.1), (2,2)}
{(3,2), (3,3)}. Compare Decision Trees, Support Vector Machines, and K-NN with respect to the number of decision boundary each approach uses!
DT: many, rectangular for numerical attributes K-NN: many, convex polygons (Voronoi cells),
SVM: one, hyperplaneK-NN is a lazy approach; what does it mean? What are the disadvantages of K-NN’s lazy approach? Do you see any advantages in using K-NN’s lazy approach.
… advantages: for quickly changing streaming data learning the model might be a waste of time and a lazy approach might be better…
Why do some support vector machine approaches map examples from a lower dimensional space to a higher dimensional space? To make them linearly separable. What is the role of slack variables in the Linear/SVM/Non-separable approach (textbook pages 266-270)—what do they measure? What properties of
hyperplanes
are maximized by the objective function f(w) (on page 268) in the approach?