Given data with no labels (e.g. only X data, no y) find clusters in that data.

Pick some number of points that you want to cluster your data into. Randomly set those "centroid" points to locations in the data space. Assign each datapoint to the centroid closest to it. Move the centroid to the center of the points assigned to it. Repeat.

In Octave:

%SETUPM = size(X,1); %K < M (because we are grouping the data into fewer clusters K_{max}= floor(M/2); %no point if less than 2 points in each cost_{old}= inf; % start with infinite cost. %The µ centroids can be set to random datapoints µr = randperm(M); % Makes a list from 1:M in random order for K = 1:K_{max}% slowly increase K % assign K cluster centroids µ_{1}, µ_{2}, ... µ_{K}, µ = X(µr(1:K), :); %Picks out first K of those values from X % Assign points to local cluster and return cost [C,cost] = groupCentriods(X, µ); if (cost < cost_{max}) break; endif if (abs(cost - cost_{old}) < cost_{elbow}) break; endif cost_{old}= cost; endfor%K-MEANS%Repeat { % How many times? % Assign points to local cluster around centroids [C,cost] = groupCentriods(X, µ); % Move cluster centroids to center of clusters data points µ = moveCentroids(X, C, K); } % and that's how you do the K-Means! function [C,cost] = groupCentriods(X, µ) for i = 1:M % for every datapoint % find C^{(i)}:= index (1:K) of cluster centroid closest to x^{(i)}d_{min}= inf; % start with infinite distance for k = 1:K % look at every cluster centroid d = sum( (X^{(i,:)}-µ^{(k,:)}).^2 ); % find distance if d < d_{min}% if this centroid is closer C^{(i)}= k; % record its number d_{min}= d; % remember how close we got endif endfor %Faster replacement for the inner (for k) loop: % d = µ - repmat(X(i,:),M,1); % [d_{min}C(i)] = min(sum(d .^ 2,2)); cost = cost + d_{min}; % accumulate the cost endfor end function µ = moveCentroids(X, C, K) for k = 1:K % for every cluster % µ_{k}:= mean position of points assigned to cluster k p = [,]; % start a list of points nearest this cluster for i = 1:M % for every datapoint if (C(i) == k) % if this point was assigned to this cluster p = [p; X(i,:)]; % save it endif endfor if length(p) > 0 µ(k,:) = mean(p); % move centroid to middle of cluster else % no data points near cluster? µ(k,:) = x(rand * M,:); % move to a new random position % could also delete endif %Faster (assuming there are no empty clusters): % p = find(C == k); %p is an array of indexes here % u(k,:) = mean(X(p,:)); endfor end

So K-Means is
minimizing
sum(||x^{(i)}-µ^{(k)}||^{2})/m

**Choosing K:** Choosing the number of clusters,
K, can be difficult to automate.

**Elbow Method:** We can try to run the method multiple times with increasing
numbers for K and perhaps find a point where the final cost has dropped rapidly
and then stops decreasing much as K increases. This is called the elbow method,
and works well if there are very obvious clusters. In other cases, it can
be very difficult to find any point where the cost decreases is significantly
less.

**Maximum Allowed Error Method: **Another way is to increase K until the
cost (maximum error in the cluster) is below some pre-set value.

**Avoid Local Clusters:** The clusters may come out differently
depending on their initial positions. In cases with a small number of K,
(less than 100, certainly less than 10) then the centroids may get stuck
in local clusters and not do a good job of finding larger clusters. To avoid
this, try multiple random initializations and test each without moving the
centroids to find the one that generates the lowest cost.

**Equal Sized Clusters:** K-Means tends to find clusters
that are the same size. In a dataset with a clusters that are larger or smaller,
this can lead to poor clustering. Expectation Maximization or EM Clustering
^avoids
this.

See also:

file: /Techref/method/ai/Clustering.htm, 5KB, , updated: 2015/8/28 15:25, local time: 2024/8/11 06:01, |

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