Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams
نویسندگان
چکیده
منابع مشابه
Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, min...
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Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by scanning the stream data; and (2) re-mining the meta-patterns to finalize and output frequent patterns. The defectiveness of such a two-phase framew...
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A data stream is a massive unbounded sequence of transactions continuously generated at a rapid rate, so how to process the transactions as fast as possible in the limited memory becomes an important problem. Although it has been studied extensively, most of the existing algorithms maintain a lot of infrequent itemsets, which causes huge space usage and inefficient update. In this paper, a new ...
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Mining frequent itemsets over a stream of transactions presents di cult new challenges over traditional mining in static transaction databases. Stream transactions can only be looked at once and streams have a much richer frequent itemset structure due to their inherent temporal nature. We examine a novel data structure, an FP-stream, for maintaining information about itemset frequency historie...
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This paper considers the problem of mining recent frequent itemsets over data streams. As the data grows without limit at a rapid rate, it is hard to track the new changes of frequent itemsets over data streams. We propose an efficient one-pass algorithm in sliding windows over data streams with an error bound guarantee. This algorithm does not need to refer to obsolete transactions when 316 C....
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ژورنال
عنوان ژورنال: Journal of Information Processing Systems
سال: 2010
ISSN: 1976-913X
DOI: 10.3745/jips.2010.6.1.079