Main.Assignments History

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November 10, 2012, at 12:02 AM EST by 206.53.114.49 -
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Assignment 5 (Due Nov 14)

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Assignment 5 (Due Dec 2)

Assignment5.pdf

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Assignment4.pdf

Assignment 5 (Due Nov 14)

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Assignment 5 (Due Nov 14)

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October 23, 2012, at 10:56 AM EST by 10.31.14.210 -
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Assignment 5 (Due Nov 14)

October 23, 2012, at 10:56 AM EST by 10.31.14.210 -
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Assignment 4 (Due Oct 21)

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Assignment 4 (Due Nov 4)

October 03, 2012, at 01:40 AM EST by 129.252.28.27 -
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Clustering and frequent itemset mining

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Clustering

October 02, 2012, at 11:17 PM EST by 129.252.28.27 -
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Assignment 3 (Due Oct 9)

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Assignment 3 (Due Oct 14)

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Assignment3.zip Δ

Assignment 4 (Due Oct 21)

Frequent itemset mining

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Frequent itemset mining

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Assignment 5 (Due Nov 4)

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September 03, 2012, at 10:43 PM EST by 206.53.114.49 -
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Assignment 5 (Due Nov 30)

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Assignment 5 (Due Nov 4)

September 03, 2012, at 10:42 PM EST by 206.53.114.49 -
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Assignment 2 (Due Sept. 23)

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Assignment 3 (Due Oct 10)

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Assignment 3 (Due Oct 9)

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August 28, 2012, at 12:37 PM EST by 10.30.13.73 -
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Assignment 1 (Due Sept. 5)

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Assignment 1 (Due Sept. 9)

August 22, 2012, at 04:37 PM EST by 129.252.11.94 -
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November 20, 2010, at 03:48 PM EST by 206.53.113.46 -
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Assignment 5

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Assignment 5 (Due Nov 30)

Homework5.pdf

November 05, 2010, at 08:51 AM EST by 129.252.11.239 -
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Assignment 4

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Assignment 4 (Due Nov 15)

Frequent itemset mining Homework4.pdf

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Assignment 3

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Assignment 3 (Due Oct 10)

September 30, 2010, at 10:02 PM EST by 206.53.113.26 -
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Clustering and frequent itemset mining

Homework3.zip

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Assignment 2

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Get familiar with Weka and KNN classifier

Assignment 2 (Due Sept. 20)

Classification using decision tree/random forest

Homework2.zip

August 30, 2010, at 01:52 AM EST by 206.53.113.26 -
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Assignment 2

Assignment 3

Assignment 4

Assignment 5

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Assignment 1 Due Sept. 5

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Assignment 1 (Due Sept. 5)

August 30, 2010, at 01:52 AM EST by 206.53.113.26 -
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Assignment 1

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Assignment 1 Due Sept. 5

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August 26, 2010, at 02:10 PM EST by 129.252.11.239 -
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Assignment 1: The Suspect Prediction Problem
Assignment Due: September 16, 12:00PM.
Download the assignment data and requirement here assignment1.zip

The police department of Los Angeles gave you a set of data to screen potential suspects. You must predict whether an individual is a suspect or not based on his/her feature information. The prediction should be reported as a probability score between 0 and 1. In this assignment, you will design a K-NN classifier to accomplish this job.

Assignment 2

Assignment 2: The Insurance Policy Customer Identification Problem
Assignment Due: September 30, 11:59pm.
Download the assignment requirement here assignment2.pdf

A Dutch insurance company wants to build a classifier system to determine whether a customer will buy its caravan insurance policy. They also want to have an explanation why.

Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set contains 4000 customers.

You are expected to build three classifiers for this problem and compare their performances. You will use the Weka data mining package. So no programming needed. Just Play the data and have fun.

Assignment 3

Hierarchical Clustering in Microarray Data Mining
Assignment Due: October 14, 11:59pm.

Clustering is a fundamental data analysis tool in many scientific research areas. In this experiment, you are expected to apply hierarchical clustering to the Microarray data mining in bioinformatics. You will apply hierarchical clustering techniques for discovering functionally related genes of given biological processes. Basically, you will do similar analysis as published in the famous paper by Michael B. Eisen et al., the most cited paper of data mining in bioinformatics, with more than 6300 citations since 1998.

Download your assignment here: assignment3.zip

Assignment 4

Dimensionality Reduction

Assignment Due: November 30th, 23:59:59pm

Download your assignment here: assignment4.zip

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August 25, 2010, at 10:46 PM EST by 206.53.113.26 -
November 19, 2007, at 03:42 PM EST by 129.252.11.79 -
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Assignment 4

Assignment 5

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Assignment 4

Dimensionality Reduction Assignment Due: November 30th, 23:59:59pm Download your assignment here: assignment4.zip

October 03, 2007, at 09:12 AM EST by 129.252.11.94 -
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Hierarchical Clustering in Microarray Data Mining

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Hierarchical Clustering in Microarray Data Mining\\

October 03, 2007, at 09:11 AM EST by 129.252.11.94 -
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Assignment Due: October 14, 11:59pm.

October 03, 2007, at 01:55 AM EST by 71.76.36.230 -
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Hierarchical Clustering in Microarray Data Mining

Clustering is a fundamental data analysis tool in many scientific research areas. In this experiment, you are expected to apply hierarchical clustering to the Microarray data mining in bioinformatics. You will apply hierarchical clustering techniques for discovering functionally related genes of given biological processes. Basically, you will do similar analysis as published in the famous paper by Michael B. Eisen et al., the most cited paper of data mining in bioinformatics, with more than 6300 citations since 1998.

Download your assignment here: assignment3.zip

September 17, 2007, at 12:12 PM EST by 129.252.11.94 -
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A Dutch insurance company wants to build a classifier system to determine whether a customer will buy its caravan insurance policy. They collected a lot of data to answer the following question: Can we predict who would be interested in buying a caravan insurance policy and give an explanation why?

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A Dutch insurance company wants to build a classifier system to determine whether a customer will buy its caravan insurance policy. They also want to have an explanation why.

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You are expected to build three classifiers for this problem and compare their performance. You will use the Weka data mining package. So no programming needed. Just Play the data and have fun.

to:

You are expected to build three classifiers for this problem and compare their performances. You will use the Weka data mining package. So no programming needed. Just Play the data and have fun.

September 17, 2007, at 12:09 PM EST by 129.252.11.94 -
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Assignment 2: The Insurance Policy Customer Identification Problem
Assignment Due: September 30, 11:59pm.
Download the assignment requirement here assignment2.pdf

A Dutch insurance company wants to build a classifier system to determine whether a customer will buy its caravan insurance policy. They collected a lot of data to answer the following question: Can we predict who would be interested in buying a caravan insurance policy and give an explanation why?

Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set contains 4000 customers.

You are expected to build three classifiers for this problem and compare their performance. You will use the Weka data mining package. So no programming needed. Just Play the data and have fun.

August 27, 2007, at 01:20 PM EST by 129.252.11.94 -
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Download the assignment data and requirement here [(attach:)|assignment1.zip]

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Download the assignment data and requirement here assignment1.zip

August 27, 2007, at 01:19 PM EST by 129.252.11.94 -
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Download the assignment data and requirement here assignment1.zip?

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Download the assignment data and requirement here [(attach:)|assignment1.zip]

August 27, 2007, at 01:18 PM EST by 129.252.11.94 -
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Download the assignment data and requirement here assignment1.zip?

The Suspect Prediction Task

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Download the assignment data and requirement here assignment1.zip?

August 27, 2007, at 01:18 PM EST by 129.252.11.94 -
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Assignment 1: The Suspect Prediction Problem
Assignment Due: September 16, 12:00PM.
Download the assignment data and requirement here assignment1.zip?

The Suspect Prediction Task

The police department of Los Angeles gave you a set of data to screen potential suspects. You must predict whether an individual is a suspect or not based on his/her feature information. The prediction should be reported as a probability score between 0 and 1. In this assignment, you will design a K-NN classifier to accomplish this job.

August 19, 2007, at 11:19 PM EST by 71.76.5.59 -
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Week 1

Week 2

Week 3

Week 4

Week 5

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Week 9

Week 10

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Assignment 1

Assignment 2

Assignment 3

Assignment 4

Assignment 5

August 19, 2007, at 07:59 PM EST by 71.76.5.59 -
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Week 5

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Week 5

Week 6

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Week 12

August 15, 2007, at 05:07 PM EST by admin -
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August 15, 2007, at 05:05 PM EST by admin -
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Not many assignments

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We will have several reading and simple programming assignments.

  • Week 1
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August 15, 2007, at 12:24 PM EST by gespim -
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Not many assignments