Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data mining applications analyze large collections of set data and high dimensional categorical data. Search on these data types is not restricted to the classic problems of mining association rules and classification, but similarity search is also a frequently applied operation. Access methods/or multidimensional numerical data are inappropriate for this problem and specialized indexes are needed.  propose a method that represents set data as bitmaps (signatures) and organizes them into a hierarchical index, suitable for similarity search and other related query types. In contrast to a previous technique, the signature tree is dynamic and does not rely on hardwired constants. Experiments with synthetic and real datasets show that it is robust to different data characteristics, scalable to the database size and efficient for various queries.
['Nikos Mamoulis', 'David W. Cheung', 'Wang Lian']
I am writing a research paper on a idea Similarity search in sets and categorical data using the signature tree
[{"from": "human", "value": "I am writing a research paper on a idea Similarity search in sets and categorical data using the signature tree"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Data mining applications analyze large collections of set data and high dimensional categorical data. Search on these data types is not restricted to the classic problems of mining association rules and classification, but similarity search is also a frequently applied operation. Access methods/or multidimensional numerical data are inappropriate for this problem and specialized indexes are needed.  propose a method that represents set data as bitmaps (signatures) and organizes them into a hierarchical index, suitable for similarity search and other related query types. In contrast to a previous technique, the signature tree is dynamic and does not rely on hardwired constants. Experiments with synthetic and real datasets show that it is robust to different data characteristics, scalable to the database size and efficient for various queries."}]