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Data mining in non-stationary data streams is particularly relevant in the context of Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) model for...
Data Mining in non-stationary data streams is gaining more attentionrecently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different typesof possibly occurring drift undermine classical assumptions such asi.i.d. data or stationary distributions. Available algorithms are either struggling with certain forms of drift or require...
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