Project #66158 - Data Mining Techniques

Q1. A database has eight transactions.  Let min-sup = 15% and min_conf = 80%.

TID

Items_bought

T001

{H,O,R,S,E}

T002

{ S, O, R, E}

T003

{H, O, N, E, Y}

T004

{O, R, T}

T005

{A, T}

T006

{H, A,T}

T007

{R, O, A, S, T}

T008

{R, O, S, E}

a.       Find all frequent itemset using Apriori.  

b.       List all the strong association rules (with support s and confidence c).

 

 

 

 

Q2. Suppose that the data mining task is to cluster points (with (x,y) representing location) into three clusters, where the points are:

O1 (3, 3)

O2 (2, 3)

O3 (2, 9)

O4 (3, 8)

O5 (7, 10)

O6 (6, 5)

O7 (1, 2)

O8 (1, 1)

O9 (3, 2)

O10 (7,  4)

O11 (5, 5)

O12 (9, 6)

The distance function is Manhattan distance. Suppose initially we assign O2,O7, andO10 as the center of each cluster, respectively.  Use the k-means algorithm to show:

1.       The three cluster centers after each round of executions.

2.       The final three clusters.

 

 

 

****Please show all work

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Due By (Pacific Time) 04/12/2015 04:00 pm
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