The immense amount of time series data produced by astronomical surveys has called for the use machine learning algorithms to discover and classify several million celestial sources. In case variable stars, supervised approaches have become commonplace. However, this needs a considerable collection expert-labeled light curves achieve adequate performance, which is costly construct. To solve pro...

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a n...

In semi supervised clustering is one of the major tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized and the similarity of objects between clusters is minimized. The dataset sometimes may be in mixed nature that is it may consist of both numeric and categorical type of data. Naturally these two types of...

Recently, semi-supervised spectral clustering algorithms have been developing rapidly, which are proposed to improve the clustering performance. In this paper, we first review the current existing spectral clustering algorithms in an unified-framework and give a straightforward explanation about the spectral clustering algorithm. Then, we present a semi-supervised method to improve the clusteri...

Both the instance level knowledge and the attribute level knowledge can improve clustering quality, but how to effectively utilize both of them is an essential problem to solve. This paper proposes a wrapper framework for semi-supervised clustering, which aims to gracely integrate both kinds of priori knowledge in the 598 J. L. Wang, S.Y. Wu, C. Wen, G. Li clustering process, the instance level...

In this paper we propose a new heuristic semi-supervised fuzzy co-clustering algorithm (SS-HFCR) for categorization of large web documents. In this approach, the clustering process is carried out by incorporating some prior knowledge in the form of pair-wise constraints provided by users into the fuzzy co-clustering framework. Each constraint specifies whether a pair of documents “must” or “can...

In genetic association studies, unaccounted population stratification can cause spurious associations in a discovery process of identifying disease-associated genetic markers. In such a situation, prior information is often available for some subjects' population identities. To leverage the additional information, we propose a semi-supervised clustering approach for detecting population stratif...

In this paper, we propose a novel method to enrich the representation provided to the output layer of feedforward neural networks in the form of an auto-clustering output layer (ACOL) which enables the network to naturally create sub-clusters under the provided main class labels. In addition, a novel regularization term is introduced which allows ACOL to encourage the neural network to reveal i...