SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. This paper extends SUSTAIN so that it can be used to model unsupervised learning data. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. Two seemingly contradictory unsupervised learning d...

Feature selection is effective in removing irrelevant data. However, the result of feature selection in unsupervised learning is not as satisfying as that in supervised learning. In this paper, we propose a novel methodology ULAC (Feature Selection for Unsupervised Learning Based on Attribute Correlation Analysis and Clustering Algorithm) to identify important features for unsupervised learning...

We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on...

A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this goal is approached by minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this w...

Unsupervised learning (clustering) deals with instances, which have not been pre-classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithms is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity....