Clustering Algorithms BIRCH and CURE - PowerPoint PPT Presentation

Title:

Clustering Algorithms BIRCH and CURE

Description:

Clustering Algorithms BIRCH and CURE Anna Putnam Diane Xu What is Cluster Analysis? Cluster Analysis is like Classification, but the class label of each object is not ... – PowerPoint PPT presentation

Number of Views:950
Avg rating:3.0/5.0
Slides: 51
Provided by: AnnaP93
Category:
Tags:
Transcript and Presenter's Notes

Title: Clustering Algorithms BIRCH and CURE

1
Clustering AlgorithmsBIRCH and CURE
• Anna Putnam
• Diane Xu

2
What is Cluster Analysis?
• Cluster Analysis is like Classification, but the
class label of each object is not known.
• Clustering is the process of grouping the data
into classes or clusters so that objects within a
cluster have high similarity in comparison to one
another, but are very dissimilar to objects in
other clusters.

3
Applications for Cluster Analysis
• Marketing discover distinct groups in customer
bases, and develop targeted marketing programs.
• Land use Identify areas of similar land use.
• Insurance Identify groups of motor insurance
policy holders with a high average claim cost.
• City-planning Identify groups of houses
according to their house type, value, and
geographical location.
• Earth-quake studies Observed earth quake
epicenters should be clustered along continent
faults.
• Biology plant and animal taxonomies, genes
functionality
• Also used for Pattern recognition, data analysis,
and image processing.
• Clustering is Studied in Data mining, statistics,
machine learning, spatial database technology,
biology, and marketing.

4
Some Requirements of Clustering in Data Mining
• Scalability
• Ability to deal with different types of
attributes
• Discovery of clusters with arbitrary shape
• Minimal requirements for domain knowledge to
determine input parameters.
• Ability to deal with noisy data
• Insensitivity to the order of input records
• High dimensionality
• Constraint-based clustering
• Interpretability and usability

5
Categorization of Major Clustering Methods
• Partitioning Methods
• Density Based Methods
• Grid Based Methods
• Model Based Methods
• Hierarchical Methods

6
Partitioning Methods
• Given a database of n objects or data tuples, a
partitioning method constructs k partitions of
the data, where each partition represents a
cluster and kn.
• It classifies the data into k groups, which
together satisfy the following requirements
• each group must contain at least one object, and
• each object must belong to exactly one group.
• Given k, the number of partitions to construct a
partitioning method creates an initial
partitioning.
• It then uses an iterative relocation technique
that attempts to improve the partitioning by
moving objects from one group to another.

7
Density Based Methods
• Most partitioning methods cluster objects based
on the distance between objects.
• Such methods can find only spherical-shaped
clusters and encounter difficulty at discovering
clusters of arbitrary density.
• The idea is to continue growing the given cluster
as long as the density (number of objects or data
points) in the neighborhood exceeds some
threshold.
• DBSCAN and OPTICS are two density based methods.

8
Grid-Based Methods
• Grid-based methods quantize the object space into
a finite number of cells that form a gird
structure.
• All the clustering operations are performed on
the grid structure.
• The advantage of this approach is fast processing
time
• STING, CLIQUE, and Wave-Cluster are examples of
grid-based clustering algorithms.

9
Model-based methods
• Hypothesize a model for each of the clusters and
find the best fit of the data to the given model.
• A model-based algorithm may locate clusters by
constructing a density function that reflects the
spatial distribution of the data points.

10
Hierarchical Methods
• Creates a hierarchical decomposition of the given
set of data objects.
• Two approaches
• agglomerative (bottom-up) starts with each
object forming a separate group. Merges the
objects or groups close to one another until all
of the groups are merged into one, or until a
termination condition holds.
• divisive (top-down) starts with all the objects
in the same cluster. In each iteration, a
cluster is split up into smaller clusters, until
eventually each object is one cluster, or until a
termination condition holds.
• This type of method suffers from the fact that
once a step (merge or split) is done, it can
never be undone.
• BIRCH and CURE are examples of hierarchical
methods.

11
BIRCH
• Balanced Iterative Reducing and Clustering Using
Hierarchies
• Begins by partitioning objects hierarchically
using tree structures, and then applies other
clustering algorithms to refine the clusters.

12
Clustering Problem
• Given the desired number of cluster K and a
dataset of N points, and a distance-based
measurement function, we are asked to find a
partition of the dataset that minimizes the value
of the measurement function.
• Due to an abundance of local minima, there is
typically no way to find a global minimal
solution without trying all possible partitions.
• Constraint The amount of memory available is
limited (typically much smaller than the data set
size) and we want to minimize the time required
for I/O.

13
Previous Work
• Probability-based approaches
• Typically make the assumption that probability
distributions on separate attributes are
statistically independent of each other.
• Makes updating and storing the clusters very
expensive.
• Distance-based approaches
• Assume that all data points are given in advance
and can be scanned frequently.
• Ignore the fact that not all data points in the
dataset are equally important with respect to the
clustering purpose.
• Are global methods. They inspect all data points
or all current clusters equally no matter how
close or far away they are.

14
Contributions of BIRCH
• BIRCH is local (instead of global). Each
clustering decision is made without scanning all
data points or currently existing clusters.
• BIRCH exploits the observation that the data
space is usually not uniformly occupied, and
therefore not every data point is equally
important for clustering purposes.
• BIRCH makes full use of available memory to
derive the finest possible subclusters while
minimizing I/O costs.

15
Definitions
• Centroid
• Diameter D
• Given the centroids of two clusters and
• -The centroid Euclidian distance D0 -The
centroid Manhattan distance D1

16
Definitions (cont.)
• Given N1 d-dimensional data points in a
cluster where i1,2,,N1, and N2
data points in another cluster where j
N11, N12,,N1N2
• -Average inter-cluster distance D2 -Average
intra-cluster distance D3
• -Variance increase distance D4

17
Clustering Feature
• A Clustering Feature (CF) is a triple summarizing
the information that we maintain about a cluster.
• N is the number of data points in the cluster
• is the linear sum of the N data points
• SS is the square sum of the N data points

18
• Assume that and
• are the CF vectors of two disjoint clusters.
Then the CF vector of th cluster that is formed
by merging the two disjoint clusters is
• From the CF definition and additivity theorem, we
know that the CF vectors of clusters can be
stored and calculated incrementally as clusters
are merged.
• We can think of a cluster as a set of data
points, but only the CF vector is stored as
summary.

19
CF Tree
• A CF tree is a height-balanced tree with two
parameters
• Branching factor B
• Threshold T
• Each nonleaf node contains at most B entries of
the form CFi,childi where i1,2,B.
• childi is a pointer to its i-th child node
• CFi is the CF of the sub-cluster represented by
this child.
• A leaf node contains at most L entries, each of
the form CFi where i1,2,,L.
• A leaf node also represents a cluster made up of
all the subclusters represented by its entries.
• All entries in a leaf node must satisfy a
threshold value T the diameter (or radius) has
to be less than T.

20
CF Tree
21
Insertion into a CF Tree
• Given entry Ent
• Identify the appropriate leaf Starting from the
root, it recursively descends the CF tree by
choosing the closest child node according to a
chosen distance metric D0,D1,D2,D3 or D4
• Modify the leaf When it reaches a leaf node, it
finds the closest leaf entry, say Li and then
tests whether Li can absorb Ent without
violating the threshold condition. If so, the CF
vector for Li is updated to reflect this. If
not, an new entry for Ent is added to the leaf.
If there is space on the leaf for this new
entry, we are done, otherwise we must split the
leaf node.

22
Insertion into a CF Tree (cont.)
• Modify the path to the leaf After inserting
Ent into a leaf, we must update the CF
information for each nonleaf entry on the path to
the leaf. Without a split, this simply involves
Ent. A leaf split requires us to insert a new
nonleaf entry into the parent node, to describe
the newly created leaf. If the parent has space
for this entry, at all higher levels, we only
need to update the CF vectors to reflect the
addition of Ent. In general, however, we may
have to split the parent as well, and so up to
the root. If the root I split, the tree height
increases by one.

23
The BIRCH Clustering Algorithm
24
Phase 1 Revisited
25
Performance of BIRCH
• Tested on three datasets used as base Work load
with D as the weighted average diameter. Each
dataset consists of K clusters of 2-d data
points. A cluster is characterized by the number
of data points in it (n), its radius (r), and its
center (c).
• Running time is basically linear wrt N, and does
not depend on the order (unlike CLARANS).

26
Performance
• In conclusion, BIRCH uses much less memory, but
is faster, more accurate, and less
order-sensitive compared with CLARANS.

BIRCH Clusters of DS1
CLARA Clusters of DS1
Actual Clusters of DS1
CLARANS Clusters of DS1
27
Application to Real Dataset
• BIRCH used to filter real images
• Two similar images of trees with a partly cloudy
sky as he background, taken at two different
wavelengths (512x1024 pixels each).
• NIR Near-infrared band
• VIS Visible wavelength band
• Soil scientists recieve hundreds of such image
pairs and try to first filter the trees from the
background, and then filter the trees into sunlit
leaves, shadows and branches for statistical
analysis.

28
Application (cont.)
• Applied BIRCH to the (NIT,VIS) value pairs for
all pixels in an image.
• Weighted equally obtain 5 clusters
• Very bright part of sky
• Ordinary part of sky
• Clouds
• Sunlit leaves
• Tree branches and shadows on the trees
• Pulled out the part of the data corresponding to
(5) and used BIRCH again. This time NIR was
weighted 10 times heavier than VIS.

29
CURE Clustering Using REpresentatives
• A Hierarchical Clustering Algorithm that Uses
Partitioning

30
Partitional Clustering
• Find k clusters optimizing some criterion
• (for example, minimize the squared-error)

31
Hierarchical Clustering
• Use nested partitions and tree structures
• Agglomerative Hierarchical Clustering
• Initially each point is a distinct cluster
• Repeated merge the closest clusters

D_avg
D_min
32
CURE
• CURE proposed by Guha, Rastogi Shim, 1998
• A new hierarchical clustering algorithm that uses
a fixed number of points as representatives
(partition)
• Centroid based approach uses 1 pt to represent
cluster gt too little information sensitive to
data shapes
• All point based approach uses all points to
cluster gt too much information sensitive to
outliers
• A constant number c of well scattered points in a
cluster are chosen, and then shrunk toward the
center of the cluster by a specified fraction
alpha
• The clusters with the closest pair of
representative points are merged at each step
• Stops when there are only k clusters left, where
k can be specified

33
Six Steps in CURE Algorithm
Draw Random Sample
Partially Cluster Partitions
Partition Sample
Data
Cluster Partial Clusters
Label Data In Disk
Eliminate Outliers
34
Example
35
• More accurate
• Adjusts well to geometry of non-spherical shapes.
• Scales to large datasets
• Less sensitive to outliers
• More efficient
• Space complexity O(n)
• Time complexity O(n2logn) (O(n2) if
dimensionality of data points is small)

36
Feature Random Sampling
• Key idea apply CURE to a random sample drawn
from the data set rather than the entire data
set.
• Smaller size
• Filtering outliers
• Concerns may miss out or incorrectly identify
certain clusters!
• Experimental results show that, with moderate
sized random samples, we were able to obtain very
good clusters.

37
Feature Partitioning for Speedup
• Partition the sample space into p partitions,
each of size n/p.
• Partially cluster each partition until the final
number of clusters in each partition reduces to
n/(pq). (q gt 1)
• Collect all partitions and run a second
clustering pass on the n/p partial clusters
• Tradeoff sample size vs. accuracy

38
Feature Labeling Data on Disk
• Input is a randomly selected sample.
• Have to assign the appropriate cluster labels to
the remaining data points
• Each data point is assigned to the cluster
containing the representative point closest to it
• Advantage using multiple points enables CURE to
correctly to distribute the data points when
clusters are non-spherical or non-union

39
Feature Outliers Handling
• Random sampling filters out a majority of the
outliers.
• The remaining few outliers in the random sample
are distributed all over the sample space and
gets further isolated.
• The clusters which are growing very slowly are
identified and eliminated as outliers.
• Use a second level pruning to eliminate
merging-together outliers outliers form very
small clusters.

40
Experiments
1. Parameter Sensitivity
2. Comparison with BIRCH
3. Scale-up

41
Dataset Setup
• Experiment with data sets
• Data set 1 contains one big and two small
circles.
• Data set 2 consists of 100 clusters with centers
arranged in a grid pattern and data points in
each cluster following a normal distribution with
mean at the cluster center.

42
Sensitivity Experiment
• CURE is very sensitive to its user-specified
parameters.
• Shrinking fraction alpha.
• Sample size s
• Number of representatives c

43
Shrinking Factor Alpha
• Alpha lt 0.2, reduces to the centroid-based
algorithm
• Alpha gt 0.7, becomes similar to the all-points
approach.
• 0.2 0.7 is a good range for alpha

44
Random Sample Size
• Tradeoff between random sample size and accuracy

45
Number of Representatives
• For smaller values of c, the quality of
clustering suffers.
• For values of c greater than 10, CURE always
found the right clusters.

46
CURE vs. BIRCH quality of clustering
• BIRCH cannot distinguish between the big and
small clusters.
• MST (all-point approach) merges the two
ellipsoids.
• CURE successfully discovers the clusters in Data
set 1.

47
CURE vs. BIRCH Execution Time
• Run both on dataset2
• CURE execution time is always lower than BIRCH
• Partitioning improves CUREs running time by gt
50
• As sample size goes up, CUREs execution time
only slightly increases due to fixed sample size

48
CURE Scale-up Experiment
49
Conclusion
• CURE and BIRCH are two hierarchical clustering
algorithms
• CURE adjusts well to clusters having
non-spherical shapes and wide variances in size.
• CURE can handle large databases efficiently.

50
Acknowledgement
• Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
CURE An Efficient Clustering Algorithm for Large
Databases. SIGMOD Conference 1998 73-84
• Jiawei Han and Micheline Kamber, Data Mining
Concepts and Techniques, Morgan Kaufmann
Publishers, 2000.
• Tian Zhang, Raghu Ramakrishnan, Miron Livny
BIRCH An Efficient Data Clustering Method for
Very Large Databases. SIGMOD Conf. 1996 103-114