# Cluster Algorithms - PowerPoint PPT Presentation

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## Cluster Algorithms

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### Fuzzy C-Means Clustering This algorithm is based upon iterative optimization of the objective function, with update of membership and cluster centers. – PowerPoint PPT presentation

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Title: Cluster Algorithms

1
Cluster Algorithms
• Sep 12th 2006

2
Goal of Clustering
• To group similar board games.
• Hundreds of board games are compared and grouped
into clusters such that they are close enough to
be converted from one board game to another.

3
Clustering Types
• Exclusive Clustering
• Eg K means
• Overlapping Clustering
• Eg Fuzzy C-means
• Hierarchical Clustering
• Eg Hierarchical clustering
• Probabilistic Clustering
• Eg Mixture of Gaussians

4
K-means Clustering Algorithm
• The algorithm is composed of the following steps
board games that are being clustered. These board
games represent the initial group centroids.
• Assign each board game to the group that has the
closest centroid.
• When all objects have been assigned, recalculate
the positions of the K centroids.
• Repeat Steps 2 and 3 until the centroids no
longer move. This produces a separation of the
objects into groups from which the metric to be
minimized can be calculated.

5
Fuzzy C-Means Clustering
• This algorithm is based upon iterative
optimization of the objective function, with
update of membership and cluster centers.
• This is based upon initial membership matrix for
each item in a cluster.
• Center of clusters are calculated based upon the
membership function.
• Once the centers are determined the membership
matrix is updated
• When the difference between two sequential
membership matrix is less than the initial
termination criterion the algorithm is stopped.
Otherwise step 2 and 3 are repeated.

6
Hierarchical Clustering Algorithms
• With a defined NN distance matrix for N board
games the following steps results into a
hierarchical cluster
• Start by assigning each item to a cluster, so
that if you have N items, you now have N
clusters, each containing just one item.
• Now start merging closest pair of clusters so
that the at merger we have one less cluster.
• Compute distances between the new cluster and
each of the old clusters.
• Repeat steps 2 and 3 until all items are
clustered into a single cluster of size N. ()
• Once the hierarchical tree is formed, it is
possible to derive k clusters from this tree by