An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data

An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data

This thesis explores various detailed improvements to semi-supervised learning (using labelled data to guide clustering or classification of unlabelled data) with fuzzy c-means clustering (a ‘soft’ clustering technique which allows data patterns to be assigned to multiple clusters using membership v...

Full description

Bibliographic Details
Main Author: Lai, Daphne Teck Ching
Format: Thesis (University of Nottingham only)
Language:English
Published: 2014
Subjects:
clustering
semi-supervised learning
breast cancer
biomedical data
Online Access:https://eprints.nottingham.ac.uk/14232/

Similar Items

Similar Items