Streaming data applications are common due to the advancement of technology to continuously capture or produce data, such as sensors for temperature, humidity and precipitation observations, social media or chatbots. These data applications receiving massive data in real-time requires an efficient algorithm and sufficient memory for analytics. Internet-of-Things (IoT) technologies embedded in a system requires a robust algorithm for clustering the streaming data to support decision making by analysing the historical sensor payloads. The MuDi-Stream algorithm, a density-based method, has emerged as one of the important methods for clustering data streams. The main issue with MuDi-Stream is the number of empty grids increased with the dimensional number or the increase of the streaming speed, making it less efficient when handling high-dimensional data. Furthermore, each point that came to a grid in the online phase will be saved, and with time, these points will consume larger memory space. To overcome these issues, we proposed an enhanced version of MuDi-Stream, coded as eMuDiS. Several benchmark datasets have been used in this study, and the performance of eMuDiS is compared to the state-of-the-art methods, including MuDi-Stream. The experimental results show that the proposed eMuDiS has better memory allocation performance than the MuDi-Stream.