Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The field of association rule mining has long been dominated by algorithms that search for patterns based on their frequency of occurrence in a given dataset. The birth of ighted association rule mining caused a fundamental paradigm shift in the way that patterns are identified. Consideration was given to the "importance" of an item in addition to its frequency of occurrence. In this research  propose a novel measure which  term Discriminatory Confidence that identifies the extent to which a given item can segment a dataset in a meaningful manner.  devise an efficient algorithm which is driven by an Information Scoring model that identifies items with high discriminatory por.  compare our results with the classical approach to association rule mining and show that the Information Scoring model produces widely divergent results. Our research reveals that mining on the basis of frequency alone tends to exclude some of the most informative patterns that are discovered using discriminatory por.
['Russel Pears', 'Yun Sing Koh', 'Gillian Dobbie']
I am writing a research paper on a idea Discriminatory confidence analysis in pattern mining
[{"from": "human", "value": "I am writing a research paper on a idea Discriminatory confidence analysis in pattern mining"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The field of association rule mining has long been dominated by algorithms that search for patterns based on their frequency of occurrence in a given dataset. The birth of ighted association rule mining caused a fundamental paradigm shift in the way that patterns are identified. Consideration was given to the \"importance\" of an item in addition to its frequency of occurrence. In this research  propose a novel measure which  term Discriminatory Confidence that identifies the extent to which a given item can segment a dataset in a meaningful manner.  devise an efficient algorithm which is driven by an Information Scoring model that identifies items with high discriminatory por.  compare our results with the classical approach to association rule mining and show that the Information Scoring model produces widely divergent results. Our research reveals that mining on the basis of frequency alone tends to exclude some of the most informative patterns that are discovered using discriminatory por."}]