KAPPA as Drift Detector in Data Stream Mining

Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...

Handling Gradual Concept Drift in Stream Data

Data streams are sequence of data examples that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques (e.g., sensor readings, call records, web page visits). Tachiniques for classification fail to successfully process data streams because of two factors: their overwhelmin...

Mining the Data Stream

Data Stream Mining

Stream Data Mining

Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations between specific values of categoric...