Farenheit 451 and other references from today's class...

Folks
 Sorry--I mis-spoke re: the title of Ray Bradbury's book. It is Farenheit 451-- (451 being the temperature where paper burns).

http://en.wikipedia.org/wiki/Fahrenheit_451

Also, we are finally coming to the part of the course that actually intersects with research done in my group (which also means I am likely to be
less objective--caveat emptor..).

Relevant publications from my group can be found at http://rakaposhi.eas.asu.edu/i3/

cheers
Rao

Re: NBC question

Probability of example--yes--that can be ignored. Just show k*what you got

Rao

ps:

[Notice that it is not all that hard to compute it too.

Suppose you are computing P(C|E)    and you write it as k*0.33
Now, suppose you also compute P(~C|E)  (where ~C means it is not in class C)-- this too will have P(E) in the denominator and so it too will have the same k factor. Suppose it is k*0.44

now, you know that P(C|E) + P(~C|E) will be 1.0.

So k*0.33+k*0.44 = 1
k = 1/0.77

On Wed, Nov 12, 2008 at 2:43 PM, Balzer, Michael J (Mike) <Michael.Balzer@asu.edu> wrote:

Hi Dr. Rao,

Regarding P(E), or, P(D) in NBC, just wanted to clarify – since this is a common factor, then it can be safely ignored, correct?  In your example slide (willwait), I think you show this as "k" in the calculation.  In the homework, do we just show this as k in the final answer?

Thanks again,

Mike Balzer

Re: Doubt about Q3 of homework 3

You use the same distance/similarity measure as in Qn 2.

Rao

On Tue, Nov 11, 2008 at 5:06 PM, Durga Bidaye <dbidaye@asu.edu> wrote:

Hello Prof Rao

I have a doubt about Q3 of Homework 3. In Q3, we are supposed to do agglomerative clustering on documents given in the previous question. Also, for inter-cluster similarity, we are supposed to use single link distance. However, nothing has been specified about the similarity measure to be used for finding the similarity(distance) between the individual documents themselves. Are we supposed to use the same measure as the previous question ie. 'Bag based similarity' or we are supposed to use something else. Thank you.

Regards
Durga

Re: Doubt Q2

Yes, taking (1-sim(c,d)) would be a fine way to convert similarity into distance (assuming of course that the similarity is between 0 and 1).

Rao

On Mon, Nov 10, 2008 at 7:25 PM, suganthi cidambaram <Suganthi.Cidambaram@asu.edu> wrote:

Dear Dr. Rao

 

Question2 asks us to find cluster dissimilarity measure ( which is defined as the sum of similarities of docs from their resp. cluster centers). But here we are using Jaccard similarity, so was wondering how would the sum of similarity give the dissimilarity. Summation seems to make sense for Euclidean Dist.

 

So should I be doing summation of (1-sim(c,d)) when using Jaccard Sim.

 

Thank You.

 

Regards

Suganthi

 

 

 

 

homework 3 assigned; due next thursday Nov 13th

Folks:
 
 I posted homework 3--with five questions. The homework is due in class next Thursday (it is a "traditional" homework in that I posted all questions at once).

To give you a heads-up, there will be one more homework before the end of the semester. For this one, I will try to add questions as soon as topics are covered in the class. The last homework will be due before the end of the semester.

Rao

Fwd: (this time with 598/494 demarcation shown) midterm grades (marks by posting-id)

---------- Forwarded message ----------
From: Subbarao Kambhampati <rao@asu.edu>
Date: Wed, Nov 5, 2008 at 12:17 PM
Subject: midterm grades (marks by posting-id)
To: Rao Kambhampati <rao@asu.edu>

Midterm
65pts	Posting ID [598Section]
31.5	1662 489
34	7309 235
32.5	2863 440
41	9443 694
40	8868 882
49.5	2568 845
57.5	4005 448
26	3902 511
53.5	1483 115
44	5123 179
57	4621 611
28	6760 290
49	2446 400
42	9882 896
46.5	7381 412
47.5	2550 408
46.5	2908 344
27	7712 852
43	6111 673
47.5	3823 434
40.5	5052 059
48	5980 640
58.5	0721 546
42.5	2429 154
31.5	9993 347
54	0819 963
22	8121 455
45	2979 440
30.5	0657 616
48.5	6348 736
54	3397 715 [494section]
27	4119 801
44.5	2816 467
45	6201 263
	2775 346
	9807 132
10	4726 012
52.5	1385 631
2
	Stats All
58.5	max
10	min
41.60	avg
11.23	stddev
	Just 598
58.5	max
22	min
42.53	avg
10.06	stddev
	just 494
52.5	max
10	min
35.8	avg
17.19	stddev

midterm grades (marks by posting-id)

Midterm
65pts	Posting ID
31.5	1662 489
34	7309 235
32.5	2863 440
41	9443 694
40	8868 882
49.5	2568 845
57.5	4005 448
26	3902 511
53.5	1483 115
44	5123 179
57	4621 611
28	6760 290
49	2446 400
42	9882 896
46.5	7381 412
47.5	2550 408
46.5	2908 344
27	7712 852
43	6111 673
47.5	3823 434
40.5	5052 059
48	5980 640
58.5	0721 546
42.5	2429 154
31.5	9993 347
54	0819 963
22	8121 455
45	2979 440
30.5	0657 616
48.5	6348 736
54	3397 715
27	4119 801
44.5	2816 467
45	6201 263
	2775 346
	9807 132
10	4726 012
52.5	1385 631
2
	Stats All
58.5	max
10	min
41.60	avg
11.23	stddev
	Just 598
58.5	max
22	min
42.53	avg
10.06	stddev
	just 494
52.5	max
10	min
35.8	avg
17.19	stddev