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- W1560505557 abstract "In the last years the datasets available have grown tremendously, and the development of efficient and scalable data mining algorithms has become a major research challenge. However, since the data is more dynamic than static there is also a strong need to update previously discovered rules and patterns. Recently, a couple of studies have emerged dealing with the topic of incremental update of discovered knowledge. These studies mostly concentrate on the question whether new rules emerge or old ones become extinct.We present a framework that enables the analyst to monitor the changes a rule may undergo when the dataset the rules were discovered from is updated, and to observe emerging trends as data change. We propose a generic rule model that distinguishes between different types of pattern changes, and provide formal definitions for these. We present our approach in a case study on the evolution of web usage patterns. These patterns have been stored in a database and are used to observe the mining sessions as snapshots across the time series of a patterns lifetime.KeywordsAssociation RuleMining SessionMining ResultTemporal ObjectNavigation PatternThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1560505557 created "2016-06-24" @default.
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- W1560505557 date "2001-01-01" @default.
- W1560505557 modified "2023-09-27" @default.
- W1560505557 title "Monitoring Change in Mining Results" @default.
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- W1560505557 doi "https://doi.org/10.1007/3-540-44801-2_6" @default.
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