Ensemble Clustering with Logic Rules

07/17/2012
by   Deniz Akdemir, et al.
0

In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive rules are used to partition the input space and an ensemble of these rules is used to define a similarity matrix. Similarity partitioning is used to partition the data in an hierarchical manner. We have used internal and external measures of cluster validity to evaluate the quality of clusterings or to identify the number of clusters.

READ FULL TEXT
research
07/15/2020

Evaluating and Validating Cluster Results

Clustering is the technique to partition data according to their charact...
research
07/08/2021

The Three Ensemble Clustering (3EC) Algorithm for Pattern Discovery in Unsupervised Learning

This paper presents a multiple learner algorithm called the 'Three Ensem...
research
06/24/2017

Semi-supervised Text Categorization Using Recursive K-means Clustering

In this paper, we present a semi-supervised learning algorithm for class...
research
07/12/2018

Rule Induction Partitioning Estimator

RIPE is a novel deterministic and easily understandable prediction algor...
research
08/14/2023

Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example

Data management and analysis tasks are often carried out using spreadshe...
research
05/21/2012

Soft Rule Ensembles for Statistical Learning

In this article supervised learning problems are solved using soft rule ...
research
01/23/2017

The Impact of Random Models on Clustering Similarity

Clustering is a central approach for unsupervised learning. After cluste...

Please sign up or login with your details

Forgot password? Click here to reset