proceed kmeans set c to intial value (which can select p1 as starting point) for each pi belong to P m(pi)=arg min distance(pi,cj) while m has changed for each i belongs to {1...n} recompute ci as the centriodof {plm(p)=i} for each pi belong to P m(pi)=arg min distance (pi,cj)
since if i use the vector i have at the moment, i would not be able to separate the data e.g. {fund A, 0.022,0.45}, {fundB, 0.432, 0.888} to compare between 0.022 and 0.432 since all data in same index.? does it mean that I have to spearate it into different vector like vector a={0.022},{0.432} vector b={0.45},{0.888} inorder to compare the distance??
Can someone give me a starting point for the algorithm since i am very new to java and algorithm, as i already set my no. of cluster to 2 and I have the selected data. I assume the first centriod point as the first point p1 e.g. 0.022. but do I compare with the next availble one in the index (0.432)?? or and I am quite confused with the min distance how I determine the min distance among all data variable and put it as same member?
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