منابع مشابه
Efficient Hypergraph Clustering
Data clustering is an essential problem in data mining, machine learning and computer vision. In this paper we present a novel method for the hypergraph clustering problem, in which second or higher order affinities between sets of data points are considered. Our algorithm has important theoretical properties, such as convergence and satisfaction of first order necessary optimality conditions. ...
متن کاملHypergraph Partitioning and Clustering
A hypergraph is a generalization of a graph wherein edges can connect more than two vertices and are called hyperedges. Just as graphs naturally represent many kinds of information in mathematical and computer science problems, hypergraphs also arise naturally in important practical problems, including circuit layout, Boolean SATisfiability, numerical linear algebra, etc. Given a hypergraph H ,...
متن کاملInhomogeneous Hypergraph Clustering with Applications
Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partitioning hyperedges across clusters. Algorithmic solutions based on this approach assume that different partitions of a hyperedge incur the same cost. However, this assumption fails ...
متن کاملNatural clustering: the modularity approach
We show that modularity, a quantity introduced in the study of networked systems, can be generalized and used in the clustering problem as an indicator for the quality of the solution. The introduction of this measure arises very naturally in the case of clustering algorithms that are rooted in Statistical Mechanics and use the analogy with a physical system. PACS numbers: PACS Numbers: 02.50.R...
متن کاملEnhancing Modularity-Based Graph Clustering
Graph clustering is defined as grouping the vertices of a given input graph into clusters. This article proposes a Two-Phase Modularity-Based Graph Clustering (2-PMGC) algorithm based on modularity optimization. The algorithm consists mainly of two steps; namely, coarsening and refinement. The coarsening phase takes the original graph as input and produces levels of coarsen graphs. The second p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2019
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0224307