What is the difference between Classification and Clustering?

Data MiningDatabaseData Structure


A classification is a data-mining approach that authorize level to a set of data to support in more efficient predictions and analysis. Classification is one of several methods predetermined to make the analysis of high datasets effective.

The "classification" is generally used when there are exactly two target classes known as binary classification. When higher than two classes can be predicted, especially in pattern recognition issues, this is defined as multinomial classification. However, multinomial classification is also used for definitive response data, where one is required to predict which category amongst multiple categories has the instances with the largest probability.

Classification is the most important element in data mining. It defines a process of assigning pre-defined class labels to instances depending on their attributes. There is a similarity among classification and clustering, it views similar, but it is different. The major difference between classification and clustering is that classification contains the leveling of items as per their membership in pre-defined groups.


The process of combining a set of physical or abstract objects into classes of the same objects is known as clustering. A cluster is a set of data objects that are the same as one another within the same cluster and are disparate from the objects in other clusters. A cluster of data objects can be considered collectively as one group in several applications. Cluster analysis is an essential human activity.

Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ways that can be useful for the objective of the analysis. This data has been used in several areas, such as astronomy, archaeology, medicine, chemistry, education, psychology, linguistics, and sociology.

There is one famous use of cluster analysis in marketing is for market segmentation: users are segmented based on demographic and transaction history data, and marketing techniques are tailored for each segment.

Cluster analysis can be used for large amounts of data. For example, Internet search engines use clustering methods to cluster queries that users submit. These can then be used for developing search algorithms.

Generally, the basic data used to cluster are a table of measurements on various variables, where each column defines a variable and a row defines a record. The aim is to form groups of data so that the same records are in the same group. The number of clusters can be pre-specified or decided from the data.

Updated on 15-Feb-2022 09:42:52