False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams
نویسندگان
چکیده
منابع مشابه
A false negative approach to mining frequent itemsets from high speed transactional data streams
Mining frequent itemsets from transactional data streams is challenging due to the nature of the exponential explosion of itemsets and the limit memory space required for mining frequent itemsets. Given a domain of I unique items, the possible number of itemsets can be up to 2 1. When the length of data streams approaches to a very large number N, the possibility of an itemset to be frequent be...
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Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summar...
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The purpose of this work is to mine closed frequent itemsets from transactional data streams using a sliding window model. An efficient algorithm IMCFI is proposed for Incremental Mining of Closed Frequent Itemsets from a transactional data stream. The proposed algorithm IMCFI uses a data structure called INdexed Tree(INT) similar to NewCET used in NewMoment[5]. INT contains an index table Item...
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Frequent itemsets mining is a fundamental primitive in data mining, requiring to identify all itemsets that appear in a fraction at least θ of the transactional dataset. However, a transactional dataset only represents a sample from the underlying process that generates the data, the understanding of which is the ultimate goal of data mining. In general, the generative process yields transactio...
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