Efficient Attribute Selection to Stand Out in the Market

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Provided by: The World
Topic: Big Data
Format: PDF
Mining of frequent item sets is one of the most fundamental problems in data mining applications. The author proposed algorithm which guides the seller to select the best attributes of a new product to be inserted in the database so that it stands out in the existing competitive products, due to budget constraints there is a limit, say m, on the number of attribute that can be selected for the entry into the database. Although the problems are NP-complete. The approximation algorithm is based on greedy heuristics.
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