Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such networks, and analyzing the complex interactions beten clusters of different types, can provide useful insights into the structure of the data. To exploit the full information content of the data, clustering approaches should consider the connections in the network as ll as the vertex attributes.  propose the density-based clustering model TCSC for the detection of clusters in heterogeneous networks that are densely connected in the network as ll as in the attribute space. Unlike previous approaches for clustering heterogeneous networks, TCSC enables the detection of clusters that show similarity only in a subset of the attributes, which is more effective in the presence of a large number of attributes.
['Brigitte Boden', 'Martin Ester', 'Thomas Seidl']
I am writing a research paper on a idea Density-Based Subspace Clustering in Heterogeneous Networks
[{"from": "human", "value": "I am writing a research paper on a idea Density-Based Subspace Clustering in Heterogeneous Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such networks, and analyzing the complex interactions beten clusters of different types, can provide useful insights into the structure of the data. To exploit the full information content of the data, clustering approaches should consider the connections in the network as ll as the vertex attributes.  propose the density-based clustering model TCSC for the detection of clusters in heterogeneous networks that are densely connected in the network as ll as in the attribute space. Unlike previous approaches for clustering heterogeneous networks, TCSC enables the detection of clusters that show similarity only in a subset of the attributes, which is more effective in the presence of a large number of attributes."}]