State of the Art Algorithms for Frequent Item Mining in Data Streams
The frequent items problem is to process a stream of items and find all those which occurs more than a given fraction of the time. It is one of the most heavily studied problems in mining data streams. The problem of detecting frequent items in streaming data is relevant to many different applications across many domains. Several algorithms, diverse in nature, have been proposed in the literature for the solution of this problem. The high complexity of the frequent item mining problem hinders the application of the stream mining techniques.