Mining frequent itemsets in a stream
Abstract
Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g., opinion and sentiment analysis from social networks. Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for...
Paper Details
Title
Mining frequent itemsets in a stream
Published Date
Jan 1, 2014
Journal
Volume
39
Pages
233 - 255
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