In line with the technological developments, the current data tends to be multidimensional and high dimensional, which is more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a new representation for the data that is smaller in volume and has the same analytical results as the original representation. To obtain an eff...

Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network pro...

Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an...

a wireless sensor network consists of many inexpensive sensor nodes that can be used toconfidently extract data from the environment .nodes are organized into clusters and in each cluster all non-cluster nodes transmit their data only to the cluster-head .the cluster-head transmits all received data to the base station .because of energy limitation in sensor nodes and energy reduction in each d...

A molybdenum-iron cluster (Mo-Fe cluster) containing 6 Fe atoms per Mo was isolated by methyl ethyl ketone extraction of component I of nitrogenase from Azotobacter vinelandii. The cluster has no EPR signal in the g = 4 region but has an intense signal at g = 2.05 and 2.01. After the cluster was transferred from methyl ethyl ketone to N-methylformamide, the signal in the g = 2 region disappeare...

Abstract. Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap: a prac...