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Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids...
Network-on-Chip (NoC) is a promising solution for System-on-Chip (SoC) challenges. In this work, we present a Decompose and Cluster generation Refinement (DCR) algorithm to find minimum power consumption simultaneously. A two-stage method is proposed for decompose and cluster generation step to generate solutions with lower power. Refinement step explores optimal positions and adjusts clusters for...
R is a free statistical programming language commonly used for the analysis of high-throughput microarray and other data. It is currently unable to easily utilise multiprocessor architectures without substantial changes to existing R scripts. Further, working with large volumes of data often leads to slow processing and even memory allocation faults. A recent survey highlighted clustering algorithms...
Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analyzing morphologically indistinguishable tumor subtypes. As such, the microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited...
In DSM era, digital circuits can contain millions placeable elements, and the complexity and the size of circuits have grown exponentially. For reducing the circuit sizes, clustering algorithm have become popular, so that the placement process can be performed faster and with higher quality. In this paper, we proposed a novel basic-pre-clustering clustering algorithm called Cell Merge which can reduce...
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