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Classification and Clustering are the two types of learning methods which characterize objects
into groups by one or more features. These processes appear to be similar, but there are some
differences between them in context of data mining.
SI Classification Clustering
1.
Classification is the process of classifying the
data with the help of class labels.
Clustering is similar to classification but
there are no predefined class labels.
2.
In classification, we have a set of predefined
classes and want to know which class a new
object belongs to.
Clustering tries to group a set of objects and
find whether there is some relationship
between the objects.
3.
In the context of machine learning,
classification is Supervised learning.
In the context of machine learning,
clustering is Unsupervised learning
4.
Training sample is provided in classification
method
Training sample is not provided in
classification method
5.
In classification, prior knowledge of classes is
already known.
In clustering, prior knowledge of classes is
not known.
6.
The tools used in classification analysis are
Decision Trees, Bayesian Classifiers,etc.
The tools mainly used in cluster analysis are
K-mean, Expectation Maximization, etc.
Example of Classification: In a banking application, the customer who applies for a loan may be
classified as a safeand risky according to his/her age and salary. This type of activity is also
called supervised learning. The constructed model can be used to classify new data. The
learning step can be accomplished by using already defined training set of data. Each record in
the training data is associated with an attribute referred to as a class label that signifies which
class the record belongs to. The produced model could be in the form of a decision tree or in a
set of rules.
Example of clustering: There are two clusters named as mammal and reptile. A mammal cluster
includes human, leopards, elephant, etc. On the other hand, reptile cluster includes snakes,
lizard, komodo dragon etc. The tools mainly used in cluster analysis are k-mean, k-medoids,
density based, hierarchical and several other methods.

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Classification vs clustering

  • 1. Classification and Clustering are the two types of learning methods which characterize objects into groups by one or more features. These processes appear to be similar, but there are some differences between them in context of data mining. SI Classification Clustering 1. Classification is the process of classifying the data with the help of class labels. Clustering is similar to classification but there are no predefined class labels. 2. In classification, we have a set of predefined classes and want to know which class a new object belongs to. Clustering tries to group a set of objects and find whether there is some relationship between the objects. 3. In the context of machine learning, classification is Supervised learning. In the context of machine learning, clustering is Unsupervised learning 4. Training sample is provided in classification method Training sample is not provided in classification method 5. In classification, prior knowledge of classes is already known. In clustering, prior knowledge of classes is not known. 6. The tools used in classification analysis are Decision Trees, Bayesian Classifiers,etc. The tools mainly used in cluster analysis are K-mean, Expectation Maximization, etc. Example of Classification: In a banking application, the customer who applies for a loan may be classified as a safeand risky according to his/her age and salary. This type of activity is also called supervised learning. The constructed model can be used to classify new data. The learning step can be accomplished by using already defined training set of data. Each record in the training data is associated with an attribute referred to as a class label that signifies which class the record belongs to. The produced model could be in the form of a decision tree or in a set of rules. Example of clustering: There are two clusters named as mammal and reptile. A mammal cluster includes human, leopards, elephant, etc. On the other hand, reptile cluster includes snakes, lizard, komodo dragon etc. The tools mainly used in cluster analysis are k-mean, k-medoids, density based, hierarchical and several other methods.