## 1 Introduction  
Optical channels in long-haul communications introduce a range of nonlinear effects that can severely degrade signal integrity [1]. Nonlinearities become more problematic in m-QAM as the order of the format increases. This leads to a highly dense set of data symbols, requiring advanced signal-processing techniques for successful symbol separation at the demodulation stage. Some well-known phenomena include Kerr-induced phase noise, four-wave mixing (FWM), and cross-phase modulation (XPM) [2, 3]. Numerous studies have explored traditional approaches that often rely on deterministic models and signal-processing techniques for mitigating nonlinear noise in optical communications [4, 5]. Recently, the state of the art has used advanced correction and demodulation strategies to maintain signal integrity. For example, there is a growing interest in machine learning techniques, which have the potential to adaptively model and mitigate nonlinearities without requiring explicit knowledge of the underlying physical processes [6, 7, 8, 9]. Representing the symbols of a transmission in a two-dimensional diagram where we can represent the power and phase, known as a constellation diagram, is a widely used approach for coherent optical communication systems. The constellation allows us to see the transmitted symbols as a snapshot of the moment that shows the phenomena affecting the channel and the distortions suffered by the grouping of symbols passing through the channel, enabling the possibility of applying machine learning techniques to the constellation. In machine learning approaches, clustering techniques have demonstrated exceptional promise for nonlinear noise mitigation [10]. Clustering is a form of unsupervised learning involving grouping similar data points based on specific criteria, such as distance metrics or density estimates [11]. Applying clustering algorithms to the data streams received by optical systems represented in a constellation diagram makes it possible to segment signal distortion symbols effectively. Clustering can be particularly advantageous in dealing with constellation working and discovering complex relationships that might not be apparent through linear analysis methods. Data streams have become pervasive across various domains, including data mining, cybersecurity, the Internet of Things (IoT), and medicine. Formally, a data stream is defined as an unbounded sequence of multidimensional, sporadic, and transient observations available over time [12]. Data streams are characterized by inherent properties such as constant evolution, rapid transmission, and a high volume, which present significant challenges for processing and analysis. These characteristics make data flows dynamic and complex, requiring advanced systems for their efficient management. Identifying the characteristics and challenges of handling data streams is essential for developing effective real-time processing tools. In this context, both supervised and unsupervised algorithms have been proposed to meet the specific demands of data stream processing. Unsupervised algorithms are widely favored due to their ability to operate without the need for training, thereby simplifying the process and reducing associated complexities. Clustering algorithms employ various approaches to handle constant evolution, high throughput, efficient memory usage, and high dimensionality of data. The review paper 'Data stream clustering: a review' [13] makes a classification of data stream clustering algorithms. It divides them into: Hierarchical algorithms, partitioning algorithms, grid-based algorithms, density-based algorithms, and model-based algorithms. Partitioning algorithms divide data into a fixed number of clusters based on centroid similarity. While relatively straightforward to implement, these algorithms can only form hyperspherical clusters as is the case with k-means, limiting their applicability in some contexts [13].