Clustering_24march_2011

# Clustering_24march_2 - Clustering TWSChow Jan2010 We must acknowledge Carnegie Mellon University as some of these slides are modified from the CMU

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Clustering  T W S Chow  Jan 2010 We must acknowledge Carnegie Mellon University as some of these slides are modified from the CMU AI Clustering ppt

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2 Outline What is Clustering? Distance Measure General Applications of Clustering Major Clustering Approaches
3 What is Clustering? Organizing data into clusters such that there is high intra-cluster similarity low inter-cluster similarity Clustering = unsupervised classification (no predefined classes) Informally, finding natural groupings among objects.

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4 What is Clustering? What is a natural grouping among these objects? Clustering is subjective Simpson's Family School Employees Females Males
5 What is Clustering? Typical usage As a stand-alone tool to get insight into data distribution As a preprocessing step for other algorithms

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6 Distance Measure What is Similarity? not easy to answer, but important. The quality or state of being similar; likeness; resemblance; as, a similarity of features. Similarity is hard to define, but… “ We know it when we see it question. We will take a more pragmatic approach . The real meaning of similarity is a philosophical
7 Cosine distance Cosine distance is a widely used measure to reflect the Euclidean distance between two investigated objects. Turn to cosine distance pdf

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9 Their similarities in this case can be measured by means of dress, height, hair, age, smoke etc. This is a topic of feature extraction.

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10 Dendogram (for hierarchical) clustering A dendogram is a cluster tree diagram In dendogram it shows where split or merge occurs It is a visualization of hierarchical clustering It enables us to specify the cutting pt for determining the number of clusters i.e., Fig. 1, we cut at 2 and obtain 2 clusters {4 objects (3,5,6 4), and 2 objects (1,2)} In Fig. 2, set to 1.2, we obtain 3 clusters. Fig. 1 Fig. 2
11 Another case clustering the blue from the reds ( hierarchical clustering )

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13 General Applications of Clustering Pattern Recognition and Image Processing Data Analysis create a nice visualizing map by clustering feature spaces

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## This note was uploaded on 04/14/2011 for the course EE 4146 taught by Professor Tommychow during the Spring '11 term at City University of Hong Kong.

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Clustering_24march_2 - Clustering TWSChow Jan2010 We must acknowledge Carnegie Mellon University as some of these slides are modified from the CMU

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