Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of impre- cise information, uncertain databases have been recently developed. ,  study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain data- base induces an exponential number of possible worlds. To tackle this problem,  propose a novel methods to capture the itemset mining process as a probability distribution func- tion taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and
['Thomas Bernecker', 'Reynold Cheng', 'David W. Cheung', 'Hans-Peter Kriegel', 'Sau Dan Lee', 'Matthias Renz', 'Florian Verhein', 'Liang Wang', 'Andreas Zuefle']
I am writing a research paper on a idea Model-based probabilistic frequent itemset mining
[{"from": "human", "value": "I am writing a research paper on a idea Model-based probabilistic frequent itemset mining"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of impre- cise information, uncertain databases have been recently developed. ,  study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain data- base induces an exponential number of possible worlds. To tackle this problem,  propose a novel methods to capture the itemset mining process as a probability distribution func- tion taking two models into account: the Poisson distribution and the normal distribution. These model-based approaches extract frequent itemsets with a high degree of accuracy and"}]