I am doing some clustering using K-means in MATLAB. As you might know the usage is as below: [IDX,C] = kmeans(X,k)
Given two points P,Q and a delta, I defined the equivalence relation ~=, where P ~= Q if EuclideanDistance(P,Q) <= delta. Now, given a set S of n points, in the example S = (A, B, C, D, E, F) and n
I have a database of user submitted latitude/longitude points and am trying to group \'close\' points together. \'Close\' is relative, but for now it seems to ~500 feet.
First find the minimum frequent patterns from the database. Then divide them into various data types like interval based , binary ,ordinal variables etc and define various distance measures for all th
if i use clustering on my google maps api, how can i get info about which markers are grouped in cluster? is it possible to get?
I want to cluster ~100,000 short strings by something like q-gram distance or simple \"bag distance\" or maybe Levenshtein distance in Python.I was planning to fill out a distance matrix (100,000 choo
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On my website I have a form that allows users to register.It asks users to provide their city, state, and country.I also have a map that drops a marker for each user based on a lat/lng that\'s drawn f
This is a tough one. There is probably a name for this and I don\'t know it, so I\'ll describe the problem exactly.
I am working towards comparing multiple images. I have these image data as column vectors of a matrix called \"images.\" I want to assess the similarity of images by first computing their Eucledian di
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