TY - JOUR

T1 - DEC

T2 - dynamically evolving clustering autonomous and its application to structure

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2014/8/14

Y1 - 2014/8/14

N2 - Identification of models from input-output data essentially requires estimation of appropriate cluster centers. In this paper, a new online evolving clustering approach for streaming data is proposed. Unlike other approaches that consider either the data density or distance from existing cluster centers, this approach uses cluster weight and distance before generating new clusters. To capture the dynamics of the data stream, the cluster weight is defined in both data and time space in such a way that it decays exponentially with time. It also applies concepts from computational geometry to determine the neighborhood information while forming clusters. A distinction is made between core and noncore clusters to effectively identify the real outliers. The approach efficiently estimates cluster centers upon which evolving Takagi-Sugeno models are developed. The experimental results with developed models show that the proposed approach attains results at par or better than existing approaches and significantly reduces the computational overhead, which makes it suitable for real-time applications.

AB - Identification of models from input-output data essentially requires estimation of appropriate cluster centers. In this paper, a new online evolving clustering approach for streaming data is proposed. Unlike other approaches that consider either the data density or distance from existing cluster centers, this approach uses cluster weight and distance before generating new clusters. To capture the dynamics of the data stream, the cluster weight is defined in both data and time space in such a way that it decays exponentially with time. It also applies concepts from computational geometry to determine the neighborhood information while forming clusters. A distinction is made between core and noncore clusters to effectively identify the real outliers. The approach efficiently estimates cluster centers upon which evolving Takagi-Sugeno models are developed. The experimental results with developed models show that the proposed approach attains results at par or better than existing approaches and significantly reduces the computational overhead, which makes it suitable for real-time applications.

U2 - 10.1109/TCYB.2013.2291234

DO - 10.1109/TCYB.2013.2291234

M3 - Journal article

VL - 44

SP - 1619

EP - 1631

JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

SN - 2168-2267

IS - 9

ER -