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» Approximate Frequency Counts over Data Streams
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KAIS
2008
150views more  KAIS 2008»
14 years 9 months ago
A survey on algorithms for mining frequent itemsets over data streams
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of freq...
James Cheng, Yiping Ke, Wilfred Ng
ICDT
2009
ACM
147views Database» more  ICDT 2009»
15 years 10 months ago
The average-case complexity of counting distinct elements
We continue the study of approximating the number of distinct elements in a data stream of length n to within a (1? ) factor. It is known that if the stream may consist of arbitra...
David P. Woodruff
DAWAK
2006
Springer
15 years 1 months ago
An Approximate Approach for Mining Recently Frequent Itemsets from Data Streams
Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that ...
Jia-Ling Koh, Shu-Ning Shin
DKE
2008
109views more  DKE 2008»
14 years 9 months ago
Deterministic algorithms for sampling count data
Processing and extracting meaningful knowledge from count data is an important problem in data mining. The volume of data is increasing dramatically as the data is generated by da...
Hüseyin Akcan, Alex Astashyn, Hervé Br...
VLDB
2002
ACM
137views Database» more  VLDB 2002»
14 years 9 months ago
Comparing Data Streams Using Hamming Norms (How to Zero In)
Massive data streams are now fundamental to many data processing applications. For example, Internet routers produce large scale diagnostic data streams. Such streams are rarely s...
Graham Cormode, Mayur Datar, Piotr Indyk, S. Muthu...