Data Classification and Incremental Clustering Using Unsupervised Learning

Title

Data Classification and Incremental Clustering Using Unsupervised Learning

Creator

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

Description

Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased becausedata mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are far away from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce.As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Insteadof selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

EAI/Springer Innovations in Communication and Computing, pp. 73-99.

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_4

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130556713&doi=10.1007%2f978-3-030-93088-2_4&partnerID=40&md5=6a07db7dff2e32b398e48cf9db3863c8