Clustering algorithms are an important branch of data mining family which has been applied widely in loT applications such as finding similar sensing patterns, detecting outliers, and segmenting large behavioral groups in real-time. Traditional full batch k-means for clustering loT big data is confronted by large scaled storage and high computational complexity problems. In order to overcome the latency inherited from full batch k-means, two big data processing methods were often used: the first method is to use small batches as the input data to multiple computers for reducing the computation efforts. However, depending on the sensed data which may be heterogeneously fused from different sources in an IoT network, the size of each mini batch may vary in each iteration of clustering process. When these input data are subject to clustering their centers would shift drastically, which affects the final clustering results. The second method is parallel computing, it decreases the runtime while the overall computational effort remains the same. Furthermore, some centroid based clustering algorithm such as k-means converges easily into local optima. In light of this, in this paper, a new partitioned clustering method that is optimized by metaheuristic is proposed for IoT big data environment. The method has three main activities: Firstly, a sample of the dataset is partitioned into mini batches. It is followed by adjusting the centroids of the mini batches of data. The third step is collating the mini batches to form clusters, so the quality of the clusters would be maximized. How the positions of the centroids could be optimally attuned at the mini batches are governed by a metaheuristic called Dynamic Group Optimization. The data are processed in parallel in Hadoop. Extensive experiments are conducted to investigate the performance. The results show that our proposed method is a promising tool for clustering fused loT data efficiently.