Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset expressing a relationship. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other, that may lead to novel discoveries regarding the objects in the dataset. This coherence leads to the possibility of obtaining representative values for rows (subset of objects) and columns (subset of attributes) of each bicluster. In the literature, it is usually studied the additive coherence among elements, i.e. each element is represented by the sum of its respective representative values. But in a given dataset, it is also possible to find multiplicative relations, i.e. each element being represented by the multiplication of its respective representative values, and that may reveal distinct knowledge contained in the objects of the dataset. So, in this paper, a swarm-based approach, named SwarmBcluster, is adapted to find both additive and multiplicative coherent biclusters from a dataset, in an attempt to enrich the amount of information provided by the biclusters. Experiments are performed considering two ll-known datasets and it is found that the multiplicative coherence biclusters improve the quality of the data analysis and may contribute to reduce the influence of noise.
['Fabrício Olivetti de França', 'Fernando J. Von Zuben']
I am writing a research paper on a idea Extracting additive and multiplicative coherent biclusters with swarm intelligence
[{"from": "human", "value": "I am writing a research paper on a idea Extracting additive and multiplicative coherent biclusters with swarm intelligence"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Biclustering is usually referred to as the process of finding subsets of rows and columns from a given dataset expressing a relationship. Each subset is a bicluster and corresponds to a sub-matrix whose elements tend to present a high degree of coherence with each other, that may lead to novel discoveries regarding the objects in the dataset. This coherence leads to the possibility of obtaining representative values for rows (subset of objects) and columns (subset of attributes) of each bicluster. In the literature, it is usually studied the additive coherence among elements, i.e. each element is represented by the sum of its respective representative values. But in a given dataset, it is also possible to find multiplicative relations, i.e. each element being represented by the multiplication of its respective representative values, and that may reveal distinct knowledge contained in the objects of the dataset. So, in this paper, a swarm-based approach, named SwarmBcluster, is adapted to find both additive and multiplicative coherent biclusters from a dataset, in an attempt to enrich the amount of information provided by the biclusters. Experiments are performed considering two ll-known datasets and it is found that the multiplicative coherence biclusters improve the quality of the data analysis and may contribute to reduce the influence of noise."}]