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...

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to ...

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...

Semi Supervised methods use a small amount of auxiliary information as a guide in the learning process in presence of unlabeled data. When using a clustering algorithm, the auxiliary information has the form of side information, that is a list of co-clustered points. Recent literature shows better performance of these methods with respect to totally unsupervised ones even with a small amount of...

Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse densit...