Data mining and data warehousing

Question One

Define Frequent Pattern Analysis and cite its applications. What are the different methods used?

Question Two

Explain in your own words the difference between “supervised learning” and “unsupervised learning”. Cite some examples of use of each one.

Question Three

Given the testing dataset and the constructed decision tree below. Calculate the accuracy, error rate, sensitivity, specificity, precision, and recall. The model predicts if a person will buy a computer or not based on his/her information.

age	income	student	credit_rating	buys_computer
<=30	high	no	fair	no
<=30	high	no	excellent	no
31…40	high	no	fair	yes
>40	medium	no	fair	no
>40	low	yes	fair	yes
>40	low	yes	excellent	no
31…40	low	yes	excellent	yes
<=30	medium	no	fair	no
<=30	low	yes	fair	yes
>40	medium	yes	fair	yes
<=30	medium	yes	excellent	no
31…40	medium	no	excellent	yes
31…40	high	yes	fair	yes
>40	medium	no	excellent	no

 

Question Four

Explain the k-means algorithm. Cite a software/program (except weka) or an online tool providing the k-means algorithm (screenshot is required).

 

Question Five

Consider the database containing transaction data as shown in the table below. Apply Apriori algorithm and find the frequent item sets where min-sup=2.

 

TID	Items Bought
1	Daiper, Bread, Juice
2	Eggs, Bread, Pasta
3	Daiper, Eggs, Bread, Pasta
4	Eggs, Pasta
5	Rice