Several clustering methods can be used in nanotechnology, each with its advantages and limitations. Some of the common methods include:
K-means clustering: This method partitions the data into k clusters, where each data point belongs to the cluster with the nearest mean. It is simple and efficient but requires the number of clusters to be specified in advance. Hierarchical clustering: This method creates a dendrogram or tree structure to represent the nested grouping of data points. It does not require the number of clusters to be specified but can be computationally intensive for large datasets. Density-based clustering: This method groups data points based on their density in the data space. It is effective for identifying clusters of varying shapes and sizes but may struggle with high-dimensional data.
