Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module

Hypergraph Clustering based on Game Theory

Data clustering considers the problem of grouping data into clusters based on its similarity measure. It is one of the central problems for data analysis, with a wide applications to a variety of areas such as marketing research, data mining and social behavior analysis.Real world objects can be modeled as points in a high dimensional metric. So clustering these real world data is corresponded ...

Hypergraph Based Clustering in High - Dimensional DataSets :

Clustering of data in a large dimension space is of a great interest in many data mining applications. In this paper, we propose a method for clustering of data in a high dimensional space based on a hypergraph model. In this method, the relationship present in the original data in high dimensional space are mapped into a hypergraph. A hyperedge represents a relationship (aan-ity) among subsets...

A normal form for hypergraph-based module extraction for SROIQ

Modularization is an important part of the modular design and maintenance of large scale ontologies. Syntactic locality modules, with their desirable model theoretic properties, play an ever increasing role in the design of algorithms for modularization, partitioning and reasoning tasks such as classification. It has been shown that, for the DL EL, the syntactic locality module extraction probl...

A Game-theory Based Clustering Approach for Wireless Sensor Networks

Clustering has long been regarded as an efficient technique for data collection in wireless sensor networks. The approach divides network into clusters and makes cluster heads (CHs) responsible for data aggregation. However, due to the constraints on available resources, a sensor node is likely to be selfish and refuse to declare itself as a cluster head. In this paper, we propose a game-theory...

Transmission cost allocation based on cooperative game theory