the first block of our unsupervised deep collaborative recommendation (UDCF) system and proposes a platform whose goal is to try find adequate parameters Kohonen maps, create homogeneous clusters in profile data results, homogeneity verified thanks very low variance rate results obtained by cluster population second criterion which high prediction recommendation. Although revision concerns only...

these are notes from introductory survey lectures given at the institute for studies in theoretical physics and mathematics (ipm), teheran, in 2008 and 2010. we present the definition and the fundamental properties of fomin-zelevinsky’s cluster algebras. then, we introduce quiver representations and show how they can be used to construct cluster variables, which are the canonical generators of ...

These are notes from introductory survey lectures given at the Institute for Studies in Theoretical Physics and Mathematics (IPM), Teheran, in 2008 and 2010. We present the definition and the fundamental properties of Fomin-Zelevinsky’s cluster algebras. Then, we introduce quiver representations and show how they can be used to construct cluster variables, which are the canonical generator...

The Open Source Cluster Application Resources (OSCAR) project is the founding working group of the Open Cluster Group (OCG). The OCG is an informal group of people dedicated to making cluster computing practical for high performance computing and more recently, clustering in general (high availability, diskless). OSCAR is a package that makes it easy to build clusters for high performance compu...

Computer cluster adoption has been slow because of the lack of single system image based cluster. OpenMosix is one instance of a successful SSI cluster. To date, there is no standard performance measurement metrics suitable for OpenMosix. A standard descriptive performance model benchmark allows developer and user to measure and compare performance of different SSI cluster design. The aim of th...

The random-cluster model has emerged as a unifying framework for studying random graphs, physical spin systems, and electrical networks [6]. Given a base graph G = (V,E), the random-cluster model consists of a probability distribution π over subsets of E, where p ∈ [0, 1] and q > 0 are real parameters determining the bond and cluster weights respectively. For q ≥ 1, there is a value of p = pc a...

Using a wireless sensor network, variety applications are being developed. In this paper, considering the field of military surveillance and reconnaissance sensors in the network for network configuration in the clustering method to elect a cluster head is about technology. Clustering of a sensor nodes elect to cluster head from among the sensor nodes and cluster heads to the sink node is confi...

Although there are many excellent clustering algorithms, effective clustering remains very challenging for large datasets that contain many classes. Image clustering presents further problems because automatically computed image distances are often noisy. We address these challenges in two ways. First, we propose a new algorithm to cluster a subset of the images only (we call this subclustering...

Previous DB research clearly concluded that the most energy-efficient configuration of a single-server DBMS is typically the highest performing one. This observation is certainly true if we focus in isolation on specific applications where the DBMS can steadily run in the peak-performance range. Because we noticed that typical DBMS activity levels—or its average system utilization—are much lowe...