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to be fine-tuned to improve performance efficiency and accuracy. For
example, in an association rule mining application, a significant frequency
change in some of the itemsets may indicate a distribution change. In
a stream classification application, a dramatic change of the size of one
class may be a signal of the impending arrival of a new distribution. Since
general approaches are not directly connected to the mining task, they
cannot take advantage of such feedback information. Hence, for many
streaming applications that only perform one specific mining task, taskspecific
techniques with outstanding performance may be more useful than
general approaches.
There is a large number of task-specific techniques developed for each
of the stream mining tasks. However, many of these techniques have
problems in meeting all of the common requirements for mining streaming
data: ability to process large volumes of data in real time, low memory
usage, and ability to cope with time-changing data. This thesis looks
into one of the most important mining tasks, which is frequency counting.
An algorithm is proposed that meet all three common requirements and
can out perform existing techniques for mining frequent itemsets in timechanging
data streams.
1.3.2 Multi-dimensional streams
Most of the stream mining techniques for dynamic data streams only work
over single-dimensional data, i.e., they assume there is only one attribute
of interest in the stream. However, the data collected in a data stream
from real-world applications usually contain several attributes. In practice,
many stream processing applications need to take more than one
attribute into consideration. For example, in modern quality control,
several quality characteristics are usually monitored simultaneously. In
e-commerce, where each data element in the stream is an order placed
by customers, a positive linear correlation between items in the order
may indicate similar purchase patterns. There has been little attention
paid to the problem of extending change detection and mining to multidimensional
streams.
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