Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Subgroup Discovery (SD) aims to nd coherent, easy-to-interpret subsets of the dataset at hand, where something exceptional is going on. Since the resulting subgroups are dened in terms of conditions on attributes of the dataset, this data mining task is ideally suited to be used by non-expert analysts. The typical SD approach uses a heuristic beam search, involving parameters that strongly inuence the outcome. Unfortunately, these parameters are often hard to set properly for someone who is not a data mining expert; correct settings depend on properties of the dataset, and on the resulting search landscape. To remove this potential obstacle for casual SD users,  introduce ROCsearch [1], a new ROC-based beam search variant for Subgroup Discovery. On each search level of the beam search, ROCsearch analyzes the intermediate results in ROC space to automatically determine a sensible search width for the next search level. Thus, beam search parameter setting is taken out of the domain expert’s hands, loring the threshold for using Subgroup Discovery. Also, ROCsearch automatically adapts its search behavior to the properties and resulting search landscape of the dataset at hand. Aside from these advantages,  also show that ROCsearch is an order of magnitude more ecient than traditional beam search, while its results are equivalent and on large datasets even better than traditional beam search results.
['Marvin Meeng', 'Wouter Duivesteijn', 'Arno J. Knobbe']
I am writing a research paper on a idea ROCsearch: an ROC-guided search strategy for subgroup discovery
[{"from": "human", "value": "I am writing a research paper on a idea ROCsearch: an ROC-guided search strategy for subgroup discovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Subgroup Discovery (SD) aims to nd coherent, easy-to-interpret subsets of the dataset at hand, where something exceptional is going on. Since the resulting subgroups are dened in terms of conditions on attributes of the dataset, this data mining task is ideally suited to be used by non-expert analysts. The typical SD approach uses a heuristic beam search, involving parameters that strongly inuence the outcome. Unfortunately, these parameters are often hard to set properly for someone who is not a data mining expert; correct settings depend on properties of the dataset, and on the resulting search landscape. To remove this potential obstacle for casual SD users,  introduce ROCsearch [1], a new ROC-based beam search variant for Subgroup Discovery. On each search level of the beam search, ROCsearch analyzes the intermediate results in ROC space to automatically determine a sensible search width for the next search level. Thus, beam search parameter setting is taken out of the domain expert\u2019s hands, loring the threshold for using Subgroup Discovery. Also, ROCsearch automatically adapts its search behavior to the properties and resulting search landscape of the dataset at hand. Aside from these advantages,  also show that ROCsearch is an order of magnitude more ecient than traditional beam search, while its results are equivalent and on large datasets even better than traditional beam search results."}]