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Agglomerative technique (top-down hierarchy of clusters) or Divisive technique (bottom-up hierarchy of clusters) are other names for hierarchical clustering.
When merging records or clusters, start by treating each data point as a separate cluster and proceed until all records have been combined into a single large cluster.
After executing the algorithm and examining the Dendrogram, a selection of clusters is made. A dendrogram is a collection of data points that resembles a multi-level nested partitioned tree of clusters.
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Work done previously cannot be undone and cannot work well on large datasets.
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Works on the basis of two parameters:
Eps - Maximum Radius of the neighbourhood
MinPts - Minimum number of points in the Eps-neighbourhood of a point
It works on the principle of density
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Works on the principle of varying density of clusters
2 Aspects for Optics
“Plot the number of clusters for the image if it was subject to Optics clustering”.
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Create a grid structure by dividing the data space into a fixed number of cells.
From the grid's cells, identify clusters.
uneven data distribution is challenging to handle.
is plagued by dimensionality, making it challenging to cluster high-dimensional data.
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STING - STatistical INformation Grid approach.
CLIQUE - CLustering in QUEst - This is both density-based as well as grid-based subspace clustering algorithm.
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Used to compare the clustering output against subject matter expertise (ground truth)
Four criteria for External Methods are:
Cluster Homogeneity - More the purity, better is the cluster formation.
Cluster Completeness - Ground truth of objects and cluster assigned objects to belong to the same cluster.
Ragbag better than Alien - Assigning heterogeneous object is very different from the remaining points of a cluster to a cluster will be penalized more than assigning it into a rag bag/miscellaneous/other category
Small cluster preservation - Splitting a large cluster into smaller clusters is much better than splitting a small cluster into smaller clusters.
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Goodness of clustering and an example of same is Silhouette coefficient
Most common internal measures:
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Compare the results of clustering obtained by different parameter settings of the same algorithm.
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