Array-based genotyping in S.cerevisiae using semi-supervised clustering

Array-based genotyping in S.cerevisiae using semi-supervised clustering

MOTIVATION Microarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short ol...

Mst-based Semi-supervised Clustering Using M-labeled Objects

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...

Semi-Supervised Polyphonic Source Identification using PLCA Based Graph Clustering

For identifying instruments or singers in the polyphonic audio, supervised probabilistic latent component analysis (PLCA) is a popular tool. But in many cases individual source audio is not available for training. To address this problem, this paper proposes a novel scheme using semisupervised PLCAwith probabilistic graph clustering, which does not require individual sources for training. The P...

Semi-supervised Clustering Using Heterogeneous Dissimilarities

Semi-Supervised Clustering Using Genetic Algorithms

A semi-supervised clustering algorithm is proposed that combines the benefits of supervised and unsupervised learning methods. Data are segmented/clustered using an unsupervised learning technique that is biased toward producing segments or clusters as pure as possible in terms of class distribution. These clusters can then be used to predict the class of future points. For example in database ...