@techreport{TD:100014,
	att_abstract={{Stream mining is a challenging problem that has attracted 
considerable attention in the last decade. As a result there are 
numerous algorithms for mining data streams, from summarizing
and analyzing, to change and anomaly 
detection. However, most research focuses on proposing, adapting or
improving algorithms and studying their computational performance. 
For a practitioner of stream mining, there is very little guidance on 
choosing a technology suited for a particular task or application. 

In this paper, we address the practical aspect of choosing a 
suitable algorithm
by drawing on the statistical properties of {em power} and
{em robustness}. For the purpose of illustration, we focus on
change detection algorithms (CDAs). We define
an objective performance measure, {it streaming power}, 
and use it to explore the robustness of three different algorithms.
The measure is
comparable for disparate algorithms, 
and provides a common framework for comparing and evaluating 
change detection algorithms on any data set
in a meaningful fashion.  We demonstrate on real world applications, 
and on synthetic 
data.

In addition, we present a
repository of data streams for the community to test change detection
algorithms for streaming data. }},
	att_authors={td3863, sk2362},
	att_categories={},
	att_copyright={{Springer}},
	att_copyright_notice={{The definitive version was published in IDA 2011. {{, 2011-12-31}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Change detection,  data streams,  statistical hypothesis testing},
	att_techdoc={true},
	att_techdoc_key={TD:100014},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100014_DS1_2011-07-21T18:47:08.038Z.pdf},
	author={Tamraparni Dasu and Gina-Maria Pomann and Shankar Krishnan},
	institution={{IDA 2011}},
	month={December},
	title={{Robustness of Stream Mining Algorithms}},
	year=2011,
}