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Cluster Analysis Notes (1)
STA 4702/5701
Spring 2009
1
The purpose of cluster analysis is to:
place observations into groups, or clusters, suggested by
the data, where groups are not defined a priori, and observations (objects) in a given cluster
tend to be similar to each other in some sense, and objects in different clusters tend to be
dissimilar.
: “
Any generalization about cluster analysis must be vague because
a vast number of clustering methods have been developed in several different fields, with
different definitions of clusters and similarity among objects. The variety of clustering
techniques is reflected by the variety of terms used for cluster analysis:
botryology,
classification,
clumping,
competitive learning,
morphometrics,
nosography,
nosology,
numerical taxonomy,
partitioning,
Qanalysis,
systematics,
taximetrics,
taxonorics,
typology,
unsupervised pattern recognition,
vector quantization,
winnertakeall learning,
aciniformics, and
agminatics.
”
Questions:
1)
Do clusters of observations naturally exist in the data?
a.
shape? overlap? number?
2)
If so, how do we identify the clusters?
a.
what method for creating clusters should be used?
b.
what measure of similarity (proximity, dissimilarity) should be used to identify
cluster membership?
3)
If so, how do we decide how many clusters there are?
4)
What are the assumptions or requirements of the clustering methods?
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View Full Document Cluster Analysis Notes (1)
STA 4702/5701
Spring 2009
2
Some Notation:
the number of observations (objects, events, experimental units) to be
clustered or grouped
the number of random variables measured on each experimental unit
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This note was uploaded on 07/22/2011 for the course STA 4702 taught by Professor Staff during the Spring '08 term at University of Florida.
 Spring '08
 Staff

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