Difference Between Classification And Clustering

Classification

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.

Clustering

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.

Difference Between Clustering and Classification

1. Type: – Clustering is an unsupervised learning method whereas classification is a supervised learning method.
2. Process: – In clustering, data points are grouped as clusters based on their similarities. Classification involves classifying the input data as one of the class labels from the output variable.
3. Prediction: – Classification involves the prediction of the input variable based on the model building. Clustering is generally used to analyze the data and draw inferences from it for better decision making.
4. Splitting of data: – Classification algorithms need the data to be split as training and test data for predicting and evaluating the model. Clustering algorithms do not need the splitting of data for its use.
5. Data Label: – Classification algorithms deal with labelled data whereas clustering algorithms deal with unlabelled data.
6. Stages: – Classification process involves two stages – Training and Testing. The clustering process involves only the grouping of data.
7. Complexity: – As classification deals with a greater number of stages, the complexity of the classification algorithms is higher than the clustering algorithms whose aim is only to group the data.