This thesis describes the development and the characteristics of the coastline along the Dutch coastal zone, managed by Hoogheemraadschap Hollands Noorderkwartier. The coastal zone is a rich environment with multiple stakeholders. These stakeholders use the coastal zone for a range of functions such as safety, nature, business, and recreation. Coastal dunes have been the first line of defense against the sea for many years. On decadal and intercentennial time scale, climate change (e.g. sea level rise) and human intervention (e.g. nourishment), affect the variability of the coastline in different ways. Reports have shown an increase of the mean sea level, which is expected to increase even after the year 2100. Bruun was one of the first researchers, who found a relation between sea level rise and shoreline recession. While Bruuns findings contain the fundamental adaption of the coastline due to sea level rise, the coastline remains a highly complex system. Morphological data has been collected yearly of the Dutch coast as part of the JarKus program. Meanwhile, the data collection and computational power have increased exponentially, while the computational cost has gone down. Combining the newly computational power with the extensive JarKus data set, provides new insight into the complex coastal system. The derived variables from the JarKus data set range from widths, gradients, volumes to heights. These derived variables are combined with nourishment into a high-dimensional data set. Clusters of comparable coastal profiles in the Hoogheemraadschap Hollands Noorderkwartier area, have been made using machine learning techniques. Machine learning techniques such as K-means and the Self-Organizing Map (SOM) contain an intelligence, with the capability of clustering similar high-dimensional objects or data, without knowing the desired output. While both methods have the same goal, K-means moves its centroids inside the data and the SOM moves the data to its centroids (BMU). The advantage of combining both methods, is to keep the topological preservation while having the 'hard' clustering advantage of the K-means, which provides easy interpretation and therefore computations. The coastline of the Noorderkwartier can be broken up into nine clusters. Five of these clusters are classified as main-clusters, having a large number of transects. Four clusters are classified as sub-clusters, having just a few transects. Each of the clusters contains its own characteristic variables. Each of these characteristics originates from its own long-term natural and human-induced causes. The variable dominating the general clustering, is the active profile of the coastline. The lesser dominating variables are the foreshore nourishment, depth of closure and increase in foreshore volume. Meaning that the high-dimensional data set, find their similarities due to these dominant variables. Upon further investigation, the clusters containing the highest active profiles, were also the clusters containing historical larger nourishments. Comparing the yearly change of the standard deviation, shows that the clusters containing larger historical nourishment, have a shifting depth of closure. With respect to the dunes, correlations are found between the dune foot, y-coordinate of the boundary between marine and aeolian transport, dune volume and active width of the dune. While the exact reason for this correlation is still unknown, it shows potential for further research. The results of this research contribute to another step in understanding the complex coastal zone. Hoogheemraadschap Hollands Noorderkwartier can now adjust its policy and approach for each cluster based on the results of this research. For further research, it is now possible to focus on specific clusters with their unique characteristics. Lastly, results highlight the importance of the effect of nourishment on the active profile and with this the future dynamic equilibrium of the coastline.