A clique clustering of a graph is a partitioning of its vertices into disjoint cliques. The quality of a clique clustering is measured by the total number of edges in its cliques. We consider the online variant of the clique clustering problem, where the vertices of the input graph arrive one at a time. At each step, the newly arrived vertex forms a singleton clique, and the algorithm can merge any existing cliques in its partitioning into larger cliques, but splitting cliques is not allowed. We give an online strategy with competitive ratio 15.645 and we prove a lower bound of 6 on the competitive ratio, improving the previous respective bounds of 31 and 2
Identifying the natural clusters of nodes in a graph and treating them as supernodes or metanodes fo...
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of ob...
We study clustering over multiple graphs- each encoding a distinct set of similarity relationships (...
A clique clustering of a graph is a partitioning of its vertices into disjoint cliques. The quality ...
Clique clustering is the problem of partitioning a graph into cliques so that some objective functio...
Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, whe...
Clique clustering is the problem of partitioning a graph into cliques so that some objective functio...
We study the online clustering problem where data items arrive in an online fashion. The algorithm m...
Abstract. In this paper, we consider the online version of the following problem: partition a set of...
We introduce a set of clustering algorithms whose performance func-tion is such that the algorithms ...
We continue the study of the online unit clustering problem, introduced by Chan and Zarrabi-Zadeh (\...
AbstractOnline unit clustering is a clustering problem where classification of points is done in an ...
ABSTRACT. In this paper, we present a general purpose network clustering algorithm based on a novel ...
In online unit clustering a set of n points of a metric space that arrive one by one, partition the ...
The goal of graph clustering is to partition objects in a graph database into different clusters bas...
Identifying the natural clusters of nodes in a graph and treating them as supernodes or metanodes fo...
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of ob...
We study clustering over multiple graphs- each encoding a distinct set of similarity relationships (...
A clique clustering of a graph is a partitioning of its vertices into disjoint cliques. The quality ...
Clique clustering is the problem of partitioning a graph into cliques so that some objective functio...
Clique clustering is the problem of partitioning the vertices of a graph into disjoint clusters, whe...
Clique clustering is the problem of partitioning a graph into cliques so that some objective functio...
We study the online clustering problem where data items arrive in an online fashion. The algorithm m...
Abstract. In this paper, we consider the online version of the following problem: partition a set of...
We introduce a set of clustering algorithms whose performance func-tion is such that the algorithms ...
We continue the study of the online unit clustering problem, introduced by Chan and Zarrabi-Zadeh (\...
AbstractOnline unit clustering is a clustering problem where classification of points is done in an ...
ABSTRACT. In this paper, we present a general purpose network clustering algorithm based on a novel ...
In online unit clustering a set of n points of a metric space that arrive one by one, partition the ...
The goal of graph clustering is to partition objects in a graph database into different clusters bas...
Identifying the natural clusters of nodes in a graph and treating them as supernodes or metanodes fo...
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of ob...
We study clustering over multiple graphs- each encoding a distinct set of similarity relationships (...