Clustering in data Mining (Data Mining)

 Atta Ul Mustafa
 Armgan Ali
 Ali raza
 Atif Ali
 Abdul Rehman
 (4425)
 4424)
 (4427)
 (4407)
 (4403)

 Introduction
 Clustering
 Why Clustering?
 Types of clustering
 Methods of clustering
 Applications of clustering

 Clustering is the process of making a group of
abstract objects into classes of similar objects.
Points to Remember
 A cluster of data objects can be treated as one
group.
 While doing cluster analysis, we first partition the
set of data into groups based on data similarity
and then assign the labels to the groups.
 The main advantage of clustering over
classification is that, it is adaptable to changes
and helps single out useful features that
distinguish different groups.

 High dimensionality - The clustering
algorithm should not only be able to handle
low- dimensional data but also the high
dimensional space.
 Ability to deal with noisy data - Databases
contain noisy, missing or erroneous data.
Some algorithms are sensitive to such data
and may lead to poor quality clusters.
 Interpretability - The clustering results
should be interpretable, comprehensible and
usable.

 Scalability - We need highly scalable
clustering algorithms to deal with large
databases.
 Ability to deal with different kind of
attributes Algorithms should be capable to be
applied on any kind of data such as interval
based (numerical) data, categorical, binary
data.
 Discovery of clusters with attribute shape -
The clustering algorithm should be capable of
detect cluster of arbitrary shape. It should not
be bounded to only distance measures that
tend to find spherical cluster of small size.

 Clustering can be divided into different categories
based on different criteria
 1.Hard clustering: A given data point in n-
dimensional space only belongs to one cluster. This
is also known as exclusive clustering. The K-Means
clustering mechanism is an example of hard
clustering.
 2.Soft clustering: A given data point can belong to
more than one cluster in soft clustering. This is also
known as overlapping clustering. The Fuzzy K-Means
algorithm is a good example of soft clustering.
 3.Hierarchial clustering: In hierarchical clustering, a
hierarchy of clusters is built using the top-down
(divisive) or bottom-up (agglomerative) approach.

 4. Flat clustering: Is a simple technique
where no hierarchy is present.
 5.Model-based clustering: In model-based
clustering, data is modeled using a standard
statistical model to work with different
distributions. The idea is to find a model that
best fits the data.

 Clustering analysis is broadly used in many applications
such as market research, pattern recognition, data
analysis, and image processing.
 Clustering can also help marketers discover distinct
groups in their customer base. And they can characterize
their customer groups based on the purchasing patterns.
 In the field of biology, it can be used to derive plant and
animal taxonomies, categorize genes with similar
functionalities and gain insight into structures inherent to
populations.
 Clustering also helps in identification of areas of similar
land use in an earth observation database. It also helps in
the identification of groups of houses in a city according
to house type, value, and geographic location.

 Clustering also helps in classifying
documents on the web for information
discovery.
 Clustering is also used in outlier detection
applications such as detection of credit card
fraud.
 As a data mining function, cluster analysis
serves as a tool to gain insight into the
distribution of data to observe characteristics
of each cluster.

 The following points throw light on why
clustering is required in data mining −
 Scalability − We need highly scalable clustering
algorithms to deal with large databases.
 Ability to deal with different kinds of attributes −
Algorithms should be capable to be applied on
any kind of data such as interval-based
(numerical) data, categorical, and binary data.
 Discovery of clusters with attribute shape − The
clustering algorithm should be capable of
detecting clusters of arbitrary shape. They should
not be bounded to only distance measures that
tend to find spherical cluster of small sizes.

 High dimensionality − The clustering
algorithm should not only be able to handle
low-dimensional data but also the high
dimensional space.
 Ability to deal with noisy data − Databases
contain noisy, missing or erroneous data.
Some algorithms are sensitive to such data
and may lead to poor quality clusters.
 Interpretability − The clustering results
should be interpretable, comprehensible, and
usable.

Clustering methods can be classified into the
following categories −
 Partitioning Method
 Hierarchical Method
 Density-based Method
 Grid-Based Method
 Model-Based Method
 Constraint-based Method

 Suppose we are given a database of ‘n’ objects and
the partitioning method constructs ‘k’ partition of
data. Each partition will represent a cluster and k ≤ n.
It means that it will classify the data into k groups,
which satisfy the following requirements −
 Each group contains at least one object.
 Each object must belong to exactly one group.
 Points to remember −
 For a given number of partitions (say k), the
partitioning method will create an initial partitioning.
 Then it uses the iterative relocation technique to
improve the partitioning by moving objects from one
group to other.

 This method creates a hierarchical
decomposition of the given set of data
objects. We can classify hierarchical methods
on the basis of how the hierarchical
decomposition is formed. There are two
approaches here −
 Agglomerative Approach
 Divisive Approach

Agglomerative Approach
 This approach is also known as the bottom-up
approach. In this, we start with each object forming a
separate group. It keeps on merging the objects or
groups that are close to one another. It keep on
doing so until all of the groups are merged into one
or until the termination condition holds.
Divisive Approach
 This approach is also known as the top-down
approach. In this, we start with all of the objects in
the same cluster. In the continuous iteration, a
cluster is split up into smaller clusters. It is down
until each object in one cluster or the termination
condition holds. This method is rigid, i.e., once a
merging or splitting is done, it can never be undone.

 This method is based on the notion of
density. The basic idea is to continue growing
the given cluster as long as the density in the
neighborhood exceeds some threshold, i.e.,
for each data point within a given cluster, the
radius of a given cluster has to contain at
least a minimum number of points.

 In this, the objects together form a grid. The
object space is quantized into finite number
of cells that form a grid structure.
Advantages
 The major advantage of this method is fast
processing time.
 It is dependent only on the number of cells in
each dimension in the quantized space.

 In this method, a model is hypothesized for
each cluster to find the best fit of data for a
given model. This method locates the clusters
by clustering the density function. It reflects
spatial distribution of the data points.
 This method also provides a way to
automatically determine the number of
clusters based on standard statistics, taking
outlier or noise into account. It therefore
yields robust clustering methods.

 In this method, the clustering is performed by
the incorporation of user or application-
oriented constraints. A constraint refers to
the user expectation or the properties of
desired clustering results. Constraints
provide us with an interactive way of
communication with the clustering process.
Constraints can be specified by the user or
the application requirement.

Clustering in data Mining (Data Mining)

Clustering in data Mining (Data Mining)

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