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Lecture 6
Clustering (Clustering algorithms (K-means clustering algorithm…
Lecture 6
Clustering
Descriptive Modelling
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Tasks
Clustering
The task of grouping objects so that the objects in the same cluster are more similar to each other than to those in other groups
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Clustering
What is clustering?
Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups
Clustering examples
In market research clustering is used to partition consumers into market segments for market segmentation
In marketing, for grouping of shopping items sets of similar products
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In Flickr's map of photos and other map sites for grouping and reducing the number of markers on a map
In image segmentation for dividing a digital image into distinct regions for border detection or object recognition
In crime analysis for identifying areas where there are greater incidences of particular types of crimes to manage law enforcement resources more effectively
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What is not clustering?
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Simple segmentation
Dividing students into different registration groups alphabetically, by last name
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Evaluating clustering
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Typically we don't know what the true clusters are, thus we cannot measure accuracies or errors
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If we knew the truth, clustering tasks would not make sense
Different clustering algorithms have their own internal evaluation measures that guide the clustering process
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Clustering algorithms
Two types of clustering
Partitional clustering
Division of data instances into non-overlapping subsets (clusters) such that each data object is in exactly one subset
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Hierarchical clustering
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Dendrogram
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