Difference between Classification and Clustering in DBMS

1. Clustering :
Database Clustering is the process of combining more than one servers or instances connecting to a single database. Sometimes one server may not be adequate to manage the amount of data or the number of requests, that is when a Data Cluster is needed.SQL is the language used to manage the database information. Clustering takes different forms, depending on how the data is stored and allocated resources.

2. Classification of Database :
Database management systems can be classified based on several criteria, such as the data model, user numbers and database distribution etc as shown in the below figure.

Difference between Classification and Clustering in DBMS :

Its all about predicting the output when input data is given. It is all about grouping data points together based on similarities among them and difference from others.
Labeled data is provided. Unlabeled data provided.
This model function classifies the data into one of defined definite classes. This function maps the data into one of the multiple clusters where the arrangement of data items is relies on the similarities between them.
In classification data are grouped by analyzing data objects whose class label is known. Clustering analyzes data objects without knowing class label.
There is some prior knowledge of attributes of each classification. There is no prior knowledge of attributes of data to form clusters.
It is done by classifying output based on the value of input data. It is done by grouping only the input data because output is not predefined.
The number of class are known before classification as there is predefined output based input data. The number of clusters are not known before clustering.These are identified after completion of clustering.
It is considered as the supervised learning because class labels are known before. It is considered as unsupervised learning because their is no prior knowledge of class label.

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