Abstract
Online mining of data streams poses many new challenges more than mining static databases. In addition to the one-scan nature, the unbounded memory requirement, the high data arrival rate of data streams and the combinatorial explosion of itemsets exacerbate the mining task. The high complexity of the frequent itemsets mining problem hinders the application of the stream mining techniques. In this review, we present a comparative study among almost all, as we are acquainted, the algorithms for mining frequent itemsets from online data streams. All those techniques immolate with the accuracy of the results due to the relatively limited storage, leading, at all times, to approximated results.
Highlights
The data generation rates in some data sources become faster than ever before
F. et al, 2006], data streams are further classified into: 1) offline data streams: which characterized by discontinuity or regular bulk arrivals [Manku G. and Motwani R., 2002], such as a bulk addition of new transactions as in a data warehouse system, and 2) online data streams: which characterized by real-time updated data that come one by one in time, such as a continuously generated transaction as in a network monitoring system
W is timebased if W consists of a sequence of fixed-length time units, where a variable number of transactions may arrive within each time unit
Summary
The data generation rates in some data sources become faster than ever before. Examples include network traffic analysis, Web click stream mining, network intrusion detection, sensor networks, web logs, and on-line transaction analysis. This rapid generation of continuous streams of information has challenged our storage, computation and communication capabilities in computing systems. Data streams differ from the conventional stored relation model in several ways: 1) Continuity: Data continuously arrive at a high rate. In the process of mining frequent itemset, traditional methods for static data usually read the database more than once. Traditional methods cannot be directly applied to data stream mining [Pauray S. and Tsai M., 2009]
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