Incremental Learning

Incremental Learning
Description	Incremental Learning processes the data one element at a time against batch methods that work on the entire set of data at a time. It usually stores a small number of elements, such as a constant number, in contrast to batch methods that store all the data.
Why to use	It generates an explicit knowledge structure that describes the clustering in a way that can be visualized and reasoned about.
When to use	To gain insights into unstructured textual data. When there is too much noise in data When the dataset is very large. When linear scalability is required.	When not to use	When data is non-spherical.
Prerequisites	Input data should be of text type and should not contain special characters and numbers.
Input	Textual Data	Output	Data divided into clusters
Statistical Methods used		Limitations	It cannot detect clustering structure as strongly as batch algorithms. Performs clustering well only with spherical data.

The incremental learning algorithm uses new input data to train the model continuously and enrich its knowledge. It is used when the training data is available periodically, or the data size is beyond the system memory limit.
This algorithm processes the data, taking one element at a time. They store a small number of elements like a constant number.

It is one of the best algorithms in terms of execution time, space, number of I/O operations, and accuracy.