Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. ,  contend that for many scenarios, it is more meaningful to assign to each object a  degree  of being an outlier. This degree is called the  local outlier factor  (LOF) of an object. It is  local  in that the degree depends on how isolated the object is with respect to the surrounding neighborhood.  give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets,  demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms  show that our approach of finding local outliers can be practical.
['Markus M. Breunig', 'Hans-Peter Kriegel', 'Raymond T. Ng', 'Jörg Sander']
I am writing a research paper on a idea LOF: identifying density-based local outliers
[{"from": "human", "value": "I am writing a research paper on a idea LOF: identifying density-based local outliers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. ,  contend that for many scenarios, it is more meaningful to assign to each object a  degree  of being an outlier. This degree is called the  local outlier factor  (LOF) of an object. It is  local  in that the degree depends on how isolated the object is with respect to the surrounding neighborhood.  give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets,  demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms  show that our approach of finding local outliers can be practical."}]