1. Distributed Information Systems (CSCI 5533) Presentation ID: 19
Clustering Algorithm
Submitted to: Dr. Liaw, Morris
Submitted by: Kumar, Manoj

2. Algorithm 5.3 BEA Input: AA : attribute affinity matrix
Output: CA : clustered affinity matrix
Begin
{ initialize; AA is an nxn matrix}
CA(*,1)← AA(*,1)
CA(*,2)← AA(*,2)
index ← 3
while index ≤ n do {choose the “best” location for attribute AAindex }

3. begin
for i from 1 to index – 1 by 1 do
calculate cont (Ai-1, Aindex, Ai)
end-for
calculate cont (Aindex-1, Aindex, Aindex+1)
loc ← placement given by maximum cont value
For j from index to loc by -1 do
CA(*,j)← CA(*,j-1)

4. CA(*,loc)← AA(*,index)
index ← index + 1
end-while
Order the rows according to the relative ordering of columns
end. {BEA}

5. AM= =

6. bond(Ax,Ay) =
AM=
A1 A2 ……. Ai-1 Ai Aj Aj+1…….. An
AM’ AM’’

7. AMold = AM’ + AM’’ + bond(Ai-1,Ai) +. bond(Ai,Aj) +bond(Aj,Ai) +
AMold = AM’ + AM’’ + bond(Ai-1,Ai) + bond(Ai,Aj) +bond(Aj,Ai) + bond(Aj,Aj+1) = +
+ 2bond(Ai,Aj)

8. AMnew = AM’ + AM’’ + bond(Ai,Ak) + bond(Ak,Ai) + bond(Ak,Aj)
+ bond(Aj,Ak)
AMnew = AM’ + AM’’ + 2bond(Ai,Ak) bond(Ak,Aj)
Cont(Ai,Ak,Aj) = AMnew – Amold
= 2bond(Ai,Ak) + 2bond(Ak,Aj) bond(Ai,Aj)

16. Questions ??