Month: March 2014

This is the first lecture about Hadoop and we need to install a couple of things to get it going. I will talk you through it in class, but here is also the todo list:Exercise19

And the lecture:Lecture19

This lecture presents a collection of different approaches to detect outliers and anomalies.
The lecture slides are here:Lecture 16
The exercise is here:Exercise 16
The files for the exercise:
linearOutlier
spatialOutlier

Cheers Arend

These are a couple of example questions, I will of course ask more and about different topics not included in this example. The questions here are intended to illustrate the kind of questions asked. Imagine these kind of questions, but about all topics covered.

You have to compare three cars, and each car has different features. Which two cars are most similar, using Jaccard index?
• A: turbo, radio, extra suspensions, 3 year warranty
• B: radio, usb port, 3 year warranty
• C: no warranty

If you would have used simple matching coefficient, would the result be different?

What are the three V in big data analysis?

Classify the three cars from the first question using the following rule based classifier:
• R1: has a radio, has a turbo -> buy
• R2: has no warranty, has no usb port -> don’t buy
• default: -> wreck

Imagine a tree building algorithm, and the data set is the one from the car example.
• List all item sets of size 2 with a support over 50%.
• What is the confidence of the following rule: {has warranty}->{usb port}?

I have the following linear equation: f(x)=0.5x+2.0
Is this the right fit for the following data set:
X Y
1 0.2
2 4.0
3 1.0

Todays lecture is about hierarchical clustering and how the result of clustering methods can be compared.
The slides are here:Lecture 15
The iPython notebook is here:cse891 – Clustering II.ipynb

Instead of a typical exercise I compiled a list of covered topics for next weeks mid term:Exercise 15

Cheers Arend