Difference between classification and clustering in data mining?

Question

Can someone explain the basic difference between classification and clustering? Provide some examples.

Answer 1

@Anisha, Following are the differences between classification and clustering-

1. Classification is the process of classifying the data with the help of class labels whereas, in clustering, there are no predefined class labels.

2. Classification is supervised learning, while clustering is unsupervised learning.

3. In Classification, algorithms like Decision trees, Bayesian classifiers are used whereas, in Clustering, algorithms like K-means, Expectation-Maximization is used.

4. Classification has prior knowledge of classes but the cluster doesn't have any prior knowledge of classes.

Example of classification: classification between gender.

Example of a cluster: discovery of patterns.

If you wish to learn more about Machine Learning, visit Machine Learning tutorial and Machine Learning course by Intellipaat.

Answer 2

Classification:

It is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood.

Classification is the process of learning a model that categorizes different predetermined classes of data. It is a two-step process, comprised of a learning step and a classification step. The learning step can be accomplished by using an already defined training set of data.

Some algorithms for classification are:

• Logistic Regression

• Decision Tree

• K-nearest neighbors (KNN)

• Support Vector Machines(SVM)

• Random Forest

Clustering:

It is a technique of organizing a group of data into classes and clusters where the objects with high similarity reside inside a cluster and the objects of two clusters would be dissimilar to each other.

Two clusters can be considered as disjoint. The main target of clustering is to divide the whole data into multiple clusters. Unlike the classification process, here the class labels of objects are not known before, and clustering is unsupervised learning.

In clustering, the similarity between two objects is measured by the similarity function where the distance between those two objects is measured. Shorter the distance higher the similarity, conversely longer the distance higher the dissimilarity.

Some algorithms for clustering are:

• K-means

• Hierarchical Clustering.

• DBSCAN

Hope this answer helps.

If you want to know more about Machine Learning then watch this video: