  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.
