Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of selecting small groups of itemsets that represent the data ll has recently gained a lot of attention.  approach the problem by searching for the itemsets that compress the data efficiently. As a compression technique  use decision trees combined with a refined version of MDL. More formally, assuming that the items are ordered,  create a decision tree for each item that may only depend on the previous items. Our approach allows us to find complex interactions beten the attributes, not just co-occurrences of 1s. Further,  present a link beten the itemsets and the decision trees and use this link to export the itemsets from the decision trees.   present two algorithms. The first one is a simple greedy approach that builds a family of itemsets directly from data. The second one, given a collection of candidate itemsets, selects a small subset of these itemsets. Our experiments show that these approaches result in compact and high quality descriptions of the data.
['Nikolaj Tatti', 'Jilles Vreeken']
I am writing a research paper on a idea Finding Good Itemsets by Packing Data
[{"from": "human", "value": "I am writing a research paper on a idea Finding Good Itemsets by Packing Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of selecting small groups of itemsets that represent the data ll has recently gained a lot of attention.  approach the problem by searching for the itemsets that compress the data efficiently. As a compression technique  use decision trees combined with a refined version of MDL. More formally, assuming that the items are ordered,  create a decision tree for each item that may only depend on the previous items. Our approach allows us to find complex interactions beten the attributes, not just co-occurrences of 1s. Further,  present a link beten the itemsets and the decision trees and use this link to export the itemsets from the decision trees.   present two algorithms. The first one is a simple greedy approach that builds a family of itemsets directly from data. The second one, given a collection of candidate itemsets, selects a small subset of these itemsets. Our experiments show that these approaches result in compact and high quality descriptions of the data."}]