Supervised Learning-Based Data Classification and Incremental Clustering

Title

Supervised Learning-Based Data Classification and Incremental Clustering

Creator

Chakraborty S.; Islam S.H.; Samanta D.

Description

Using supervised learning-based data classification and incremental clustering, an unknown example can be classified using the most common class among K-nearest examples. The KNN classifier claims, Tell me who your neighbors are, and it will tell you who you are. The supervised learning-based data classification and incremental clustering technique is a simple yet powerful approach with applications in computer vision, pattern recognition, optical character recognition, facial recognition, genetic pattern recognition, and other fields. Its also known as a slacker learner because it doesnt develop a model to classify a given test tuple until the very last minute. When we say yes or no, there may be an element of chance involved. However, the fact that a diner can recognise an invisible food using his senses of taste, flavour, and smell is highly fascinating. At first, there can be a brief data collection phase: what are the most noticeable spices, aromas, and textures? Is the flavour of the food savoury or sweet? This information can then be used by the diner to compare the bite to other items he or she has had in the past. Earthy flavours may conjure up images of mushroom-based dishes, while briny flavours may conjure up images of fish. We view the discovery process through the lens of a slightly modified adage: if it smells like a duck and tastes like a chicken, youre probably eating chicken. This is a case of supervised learning in action. Machine learning can benefit from supervised learning, which is a concept that can be applied to it (ML). 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

EAI/Springer Innovations in Communication and Computing, pp. 33-72.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Coverage

Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 25228595

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-3-030-93088-2_3

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