This document discusses unsupervised machine learning classification through clustering. It defines clustering as the process of grouping similar items together, with high intra-cluster similarity and low inter-cluster similarity. The document outlines common clustering algorithms like K-means and hierarchical clustering, and describes how K-means works by assigning points to centroids and iteratively updating centroids. It also discusses applications of clustering in domains like marketing, astronomy, genomics and more.
4. Clustering - Definition
─ Process of grouping similar items together
─ Clusters should be very similar to each other
but…
─ Should be very different from the objects of other
clusters/ other clusters
─ We can say that intra-cluster similarity between
objects is high and inter-cluster similarity is low
─ Important human activity --- used from early
childhood in distinguishing between different
items such as cars and cats, animals and plants
etc.
5. Supervised and Unsupervised Classification
─ What is Classification?
─ What is Supervised Classification/Learning?
─ What is Unsupervised Classification/Learning?
─ SOM – Self Organizing Maps
6. Types of Clustering Algorithms
─ Clustering has been a popular area of research
─ Several methods and techniques have been
developed to determine natural grouping among
the objects
Jain, A. K., Murty, M. N., and Flynn, P. J., Data Clustering: A Survey.
ACM Computing Surveys, 1999. 31: pp. 264-323.
Jain, A. K. and Dubes, R. C., Algorithms for Clustering Data. 1988,
Englewood Cliffs, NJ: Prentice Hall. 013022278X
7. Types of Clustering Algorithms
Hierarchical
Methods
Partitioning
Methods
Grid-Based
Methods
Clustering
Algorithms Used in
Machine Learning
Algorithms For
High Dimensional
Data
Agglomerative
Algorithms
Divisive
Algorithms
Relocation
Algorithms
Probabilistic
Clustering
K-medoids
Methods
K-means Methods Density-Based
Algorithms
Density-Based
Connectivity
Clustering
Density Functions
Clustering
Gradient Descent
and Artificial
Neural Networks
Evolutionary
Methods
Subspace
Clustering
Co-Clustering
Techniques
Projection
Techniques
Clustering
Hierarchical
Methods
Partitioning
Methods
Grid-Based
Methods
Clustering
Algorithms Used in
Machine Learning
Algorithms For
High Dimensional
Data
Hierarchical
Methods
Partitioning
Methods
Grid-Based
Methods
Clustering
Algorithms Used in
Machine Learning
Algorithms For
High Dimensional
Data
Agglomerative
Algorithms
Divisive
Algorithms
Agglomerative
Algorithms
Divisive
Algorithms
Relocation
Algorithms
Probabilistic
Clustering
K-medoids
Methods
K-means Methods Density-Based
Algorithms
Relocation
Algorithms
Probabilistic
Clustering
K-medoids
Methods
K-means Methods Density-Based
Algorithms
Density-Based
Connectivity
Clustering
Density Functions
Clustering
Density-Based
Connectivity
Clustering
Density Functions
Clustering
Gradient Descent
and Artificial
Neural Networks
Evolutionary
Methods
Gradient Descent
and Artificial
Neural Networks
Evolutionary
Methods
Subspace
Clustering
Co-Clustering
Techniques
Projection
Techniques
Clustering
10. Clustering Evaluation
• Manual inspection
• Benchmarking on existing labels
• Cluster quality measures
–distance measures
–high similarity within a cluster, low across
clusters
11. The Distance Function
• Simplest case: one numeric attribute A
– Distance(X,Y) = A(X) – A(Y)
• Several numeric attributes:
– Distance(X,Y) = Euclidean distance between
X,Y
• Are all attributes equally important?
– Weighting the attributes might be necessary
12. Simple Clustering: K-means
Works with numeric data only
1) Pick a number (K) of cluster centers (at
random)
2) Assign every item to its nearest cluster
center (e.g. using Euclidean distance)
3) Move each cluster center to the mean of
its assigned items
4) Repeat steps 2,3 until convergence
(change in cluster assignments less than
a threshold)
22. K-means variations
• K-medoids – instead of mean, use
medians of each cluster
–Mean of 1, 3, 5, 7, 9 is
–Mean of 1, 3, 5, 7, 1009 is
–Median of 1, 3, 5, 7, 1009 is
–Median advantage: not affected by extreme
values
• For large databases, use sampling
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205
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29. K-means clustering summary
Advantages
• Simple, understandable
• items automatically
assigned to clusters
Disadvantages
• Must pick number of
clusters before hand
• All items forced into a
cluster
• Too sensitive to outliers
since an object with an
extremely large value
may substantially
distort the distribution
of data
30. Hierarchical clustering
• Agglomerative Clustering
– Start with single-instance clusters
– At each step, join the two closest clusters
– Design decision: distance between clusters
• Divisive Clustering
– Start with one universal cluster
– Find two clusters
– Proceed recursively on each subset
– Can be very fast
• Both methods produce a
dendrogram
g a c i e d k b j f h
32. A two dimensional image of supervised clusters (real case)
Partial Supervision of Clustering
33. Partial Supervision of Clustering
5
4
3
2
1
5
4
3
2
1
Disputed Data
Point
A two dimensional image of the different zones of overlapping clusters
who both claim a data point (More than two clusters claiming a point is
also common)
34. Research Problems
─ Effective and Efficient methods of Clustering
─ Scalability
─ Handling different types of data
─ Handling complex multidimensional data
─ Complex shapes of clusters
─ Subspace Clustering
─ Cluster overlapping etc.
35. Examples of Clustering Applications
• Marketing: discover customer groups and use
them for targeted marketing and re-organization
• Astronomy: find groups of similar stars and
galaxies
• Earth-quake studies: Observed earth quake
epicenters should be clustered along continent
faults
• Genomics: finding groups of gene with similar
expressions
• …
36. Clustering Summary
• unsupervised
• many approaches
–K-means – simple, sometimes useful
• K-medoids is less sensitive to outliers
–Hierarchical clustering – works for symbolic
attributes
–Can be used to fill in missing values