An overview of hierarchical and non-hierarchical algorithms of clustering for semi-supervised classification

Authors:
Sadaaki Miyamoto
Affiliations:
Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Venue:
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Year:
2012

Citing 10
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Constrained K-means Clustering with Background Knowledge

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach

Machine Learning
Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results

Data Mining and Knowledge Discovery
Introduction to Semi-Supervised Learning

Introduction to Semi-Supervised Learning
Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications

Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications
Semi-Supervised Learning

Semi-Supervised Learning
Agglomerative hierarchical clustering with constraints: theoretical and empirical results

PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

An overview of a variety of methods of agglomerative hierarchical clustering as well as non-hierarchical clustering for semi-supervised classification is given. Two different formulations for semi-supervised classification are introduced: one is with pairwise constraints, while the other does not use constraints. Two methods of the mixture of densities and fuzzy c-means are contrasted and their theoretical properties are discussed. A number of agglomerative hierarchical algorithms are then discussed. It will be shown that the single linkage has different characteristics when compared with the complete linkage and average linkage. Moreover the centroid method and the Ward method are discussed. It will also be shown that the must-link constraints and the cannot-link constraints are handled in different ways in these methods.