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Given a graph G = (V, E), a node is called perfect (with respect to a set S ⊆ V) if its closed neighborhood contains exactly one node in set S, a node is called nearly perfect if it is not perfect but is adjacent to a perfect node. S is called a perfect neighborhood set if each node is either perfect or nearly perfect. We present the first self-stabilizing algorithm for computing a perfect neighborhood...
The general focus of this study is to design a multilevel deep learning model that provides big data analytics and emergency management knowledge. A big data covariance analysis approach has been used to find multilevel representations of data based on prior knowledge from large scale power systems. For purpose of meeting requirements of incremental knowledge discovery, an adaptive regression algorithm...
Selection of Influential Users is an essential task in influence propagation schemes in social networks. Considering the phenomenon of asymmetric influences between neigh boring users and different sensitivities of users to influences, we propose to select the users in the minimal weighted positive influence dominating set of a social network graph as the influential users to maximize the speed of...
In a graph or a network G = (V, E), a set S ⊆ V is a 2-packing if ∀i ∊ V : |N[i]∩S| ≤ 1, where N[i] denotes the closed neighborhood of node i. A 2-packing is maximal if no proper superset of S is a 2-packing. This paper presents a safely converging self-stabilizing algorithm for maximal 2-packing problem. Under a synchronous daemon,...
In microarray data, a bicluster refers to a subset of genes exhibiting consistent patterns over a subset of conditions. In this paper, we propose a method for detecting these biclusters in large gene expression datasets. We consider the bicluster patterns based geometric relations. We use Randomized Hough Transform for sub-bicluster detection in column pair spaces and a spectra graph based combination...
Biclustering can perform simultaneous pattern classification in both row and column directions in a data matrix and is useful for DNA microarray data analysis. In this paper, a new biclustering method is introduced based on a geometrical method of identifying bicluster patterns. The Hough transform in column-pair space is used to find sub-biclusters and a hypergraph model is used to merge the sub-biclusters...
K-means algorithm is one of the most popular clustering algorithms. However, it is sensitive to initialized partition and the circular dataset. To attack this problem, this paper introduced an improved k-means algorithm based on multiple feature points. The algorithm selects a number of feature points as cluster centroids unlike the traditional algorithm which only uses one centroid. In addition,...
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