Ensemble framework for concept-drift detection in multidimensional streaming data

ABSTRACT

The potential objective of data mining (DM) over the data streaming is the detection of concept-drift. Concept-Drift signifies a diversity among the data tuples streamed in the sequence. The concept-drift often appears as incremental or abrupt. The incremental drift denotes the gradual increment of the drift between the tuples of streaming data. The other format of the drift is abrupt, which signifies the drift between tuples of data streaming in sequence. The proposed method is an Ensemble Framework for Concept-Drift Detection in Multidimensional Streaming Data (EFCDD). In addition, the proposed method EFCDD deals with the recurrent drift of the concept in streaming data. To state the drift, the projection diversity of the values representing the field positions or field-IDs, which are in use for framing the structure of the records streaming form the intended sources. The experimental study was carried out by mocking the streams of those transmitting records of the benchmark datasets often used in DM. The outcomes of the experimental study evince the scalability and prominence of EFCDD toward the detection of drift in concept. The proposal performance is measured by comparing simulation outcomes with the other existing model.

KEYWORDS:

• Data mining
• multidimensional
• online imbalance datasets
• streaming data
• EX-Stream

Disclosure statement

No potential conflict of interest was reported by the authors.