Ensemble Clustering with Logic Rules
This is an incremental method for clustering tasks in machine learning, with no specific problem or audience mentioned.
The paper tackles unsupervised or semi-supervised clustering by applying logic rule ensembles to partition data and define a similarity matrix, resulting in hierarchical clustering evaluated using internal and external validity measures, though no concrete numbers are provided.
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.