12/28/2017 Data Mining Cluster Analysis
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DATA MINING - CLUSTER ANALYSIS
Cluster is a group of objects that belongs to the same class. In other words, similar objects are grouped in one
cluster and dissimilar objects are grouped in another cluster.
What is 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.
Applications of Cluster Analysis
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.
Requirements of Clustering in Data Mining
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 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.
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