We consider the problem of data clustering with unidentified feature quality and when a small amount labelled is provided. An unsupervised sparse method can be employed in order to detect subgroup features necessary for semi-supervised use create constraints enhance solution. In this paper we propose K-Means variant that employs these techniques. show algorithm maintains high performance other ...

that mean extraction generalizes to other emotions (Figure S1A) and even other face dimensions, such as gender (Figure S1B). In these cases, mean discrimination performance was at least as good as regular discrimination. Further precise mean discrimination occurred for stimulus durations as brief as 500 ms (Figure S3). It is unlikely that the mean extraction revealed here was driven by feature-...

Since advanced technologies via social media, internet, virtual communities and networks internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as natural design for data. K-means (KM) (single-view) had been extended handling data, called KM (MV-KM). In the literature, most MV-KM algorithms reported influenced by initializations als...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it a...