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.