Multi Domain Graph Clustering

Co regularized multi domain graph clustering cgc.

Multi domain graph clustering. Flexible and robust co regularized multi domain graph clustering. The cross domain instance relationships are only used to enhance the clustering result within each domain. Multi view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains.

Sullivan 3 and wei wang 4 1 department of computer science university of north carolina at chapel hill nc 27599 usa 2 department of electrical engineering and computer science case western reserve university oh 44106 usa 3 departments of genetics and. Some other multi domain graph network clustering methods focus on improving the clustering accuracy within each domain utilizing information from other domains. Integrating co regularized multi domain graph for clustering 46 3 fig.

Sullivan3 and wei wang4 1department of computer science university of north carolina at chapel hill nc 27599 usa 2department of electrical engineering and computer science case western reserve university oh 44106 usa 3departments of genetics and psychiatry. Despite the previous success existing multi view graph clustering methods usually. Leveraging cross domain information has been demonstrated an effective way to achieve better clustering results.

They do not capture associations between clusters across multiple domains. Gene protein and genetic variant domains can be many to many. Previous chapter next chapter.

Multi view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Flexible and robust co regularized multi domain graph clustering wei cheng1 xiang zhang2 zhishan guo1 yubao wu2 patrick f.

For example multi ple proteins may be synthesized from one gene and one gene may contain many genetic variants.