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over dynamic streams must find a “balance” between efficiency and accuracy.
In some cases, some accuracy may be sacrificed to achieve higher
efficiency, and vise versa. A more detailed survey of the related work on
change detection will be given in Section 3.2.
2.3 Survey on data stream mining
Data stream mining is the process of extracting information and patterns
from streaming data. It can be considered as an extension of traditional
data mining and knowledge discovery from relational tables to the new
type of continuous, unbounded, rapid, and time-changing data. Therefore,
most of the issues identified in relational data mining must also be
addressed for stream mining, with the additional difficulties introduced by
new stream data as discussed in Chapter 1.
2.3.1 Data refining
Data refining approaches refine the data elements in the stream for the
purpose of mining. These approaches do not extract complex information
from streams, but a data stream after these processes will be cleaner, more
compact, and better structured. Mining the stream after these processes
may greatly improve the performance of the mining applications. Some
information generated from the processes, such as data synopsis and distribution
change alarms, can help the mining applications to adjust their
parameters and strategies over time. Unlike relational data mining that
process the entire data once, before the mining procedure starts, streaming
data processing procedures continue during the entire life span of streams.
Sampling
Data stream sampling is the process of choosing a suitable representative
subset from the stream of interest. The major purpose of stream sampling
is to reduce the potentially infinite size of the stream to a bounded set of
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