Finding an efficient data reduction method for largescale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automat...

In this work, we present a set of techniques that considerably improve the performance of executing concurrent MapReduce jobs. Our proposed solution relies on proper resource allocation for concurrent Hive jobs based on data dependency, inter-query optimization and modeling of Hadoop cluster load. To the best of our knowledge, this is the first work towards Hive/MapReduce job optimization which...

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mixture of factor analyzers, carrying out simultaneous clustering and locally l...

Nonlinear dimensionality reduction techniques for manifold learning, e.g., Isomap, may become exceedingly expensive to carry out for large data sets. This paper explores a multilevel framework with the goal of reducing the cost of unsupervised manifold learning. In addition to savings in computational time, the proposed multilevel technique essentially preserves the geodesic information, and so...

In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis. Such dimension reduction can be very efficient when the signals of interest have a strong structure, such as with images. We focus on this setting and investigate feature clust...

We introduce a view of unsupervised learning that integrates probabilistic and nonprobabilistic methods for clustering, dimensionality reduction, and feature extraction in a unified framework. In this framework, an energy function associates low energies to input points that are similar to training samples, and high energies to unobserved points. Learning consists in minimizing the energies of ...

As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular in nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this paper, Isomap, one of the most famous manifold learning algorithms, is applied to process closing prices of stocks of CSI 300 index from September 2009 to October 2011....

We study the phenomenon of cluster synchrony that occurs in ensembles of coupled phase oscillators when higher-order modes dominate the coupling between oscillators. For the first time, we develop a complete analytic description of the dynamics in the limit of a large number of oscillators and use it to quantify the degree of cluster synchrony, cluster asymmetry, and switching. We use a variati...

In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation schemes which affect the performance of the system. In this paper we test the pre-existing proposed algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic and cluster approaches are proposed along with the optimization using bio-inspired algori...