JA-BE-JA: A distributed algorithm for balanced graph Partitioning 2013, International Conference on Self-Adaptive and Self-Organizing Systems, SASO A mltilevel memetic approach for improving graph k-partitions 2011, IEEE Transactions on Evolutionary Computation View all citing articles on ScopusView...
The idea of neighborhood construction emerged as an approach for improving the quality of the results of classifying and clustering algorithms. However, we think that a comprehensive study of the neighborhood construction algorithm is very useful. To the best of our knowledge, this is the first ...
In the paper a directed graph connected with an ordered pair of ordered partitions is constructed. Some properties of such graphs in the case of the conjugated partitions are considered.doi:10.1007/BF01159532L. M. KoganovKluwer Academic Publishers-Plenum PublishersMathematical Notes of the Academy ...
The value-proposition of this document is therefore, on a first level, the identification of the dimensions we observe to be relevant with respect to graph processing. This is more complex than, for example, merely listing the types of graph processing system architectures or the types of commun...
Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number ...
RUN Ncut_test.m to determine the optimal partition from varied partitions (produced by Jianbo Shi's Normalized cuts). CVDD.mincludes Algorithm 1: CVDD in our paper. Ncut_test.mas an example includes Algorithm 2: CVDD-OP in our paper. ...
Algorithm 1: The proposed SC algorithm. Input: ℵ, 𝐤k, k▹ℵ—a set of points, nearest neighbors for affinity matrix and number of clusters Output: Cluster labels of all the points 1. Compute the KNN graph and the weight matrix A using (3)–(4) 2. To get the normalized grap...
Keywords: automatic K-means clustering; adaptive genetic algorithm; improved K-means++; density estimation; taxi GPS data 1. Introduction Data clustering is an important and well-known technique in the area of unsupervised machine learning. It is used for identifying similar records in one cluster ...
WS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the ...
Most implementation of algorithms do not scale to tens or hundreds of cores (let alone thousands or millions,) even if in theory the algorithm itself is reasonably easy to parallelize. The world’s fastest single machine (by a margin of 2x from the next competitor,) the Chinese Tianhe-2, ...