Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most existing clustering algorithms cluster highly related data objects such as b pages and b users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. ,  propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.
['Jidong Wang', 'Hua-Jun Zeng', 'Zheng Chen', 'Hongjun Lu', 'Li Tao', 'Wei-Ying Ma']
I am writing a research paper on a idea ReCoM: reinforcement clustering of multi-type interrelated data objects
[{"from": "human", "value": "I am writing a research paper on a idea ReCoM: reinforcement clustering of multi-type interrelated data objects"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most existing clustering algorithms cluster highly related data objects such as b pages and b users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. ,  propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy."}]