Understanding Extractive Techniques in Data Mining and Machine Learning

Extractive refers to the process of extracting or removing something from a larger context or whole. In the context of data mining and machine learning, extractive techniques are used to selectively extract relevant information or features from a large dataset, rather than using the entire dataset.

For example, in natural language processing, extractive techniques might be used to extract specific keywords or phrases from a document, or to identify the main topics or themes present in a text. In image analysis, extractive techniques might be used to extract specific features or objects from an image, such as edges, corners, or shapes.

The goal of extractive techniques is to reduce the complexity of the data and to identify the most important or relevant information, which can then be used for further analysis or processing. Extractive techniques are often contrasted with transformative techniques, which modify or transform the data in some way, rather than simply selecting certain aspects of it.