Which clustering algorithm is suitable for one-dimensional Lists without knowing k?
c#, cluster-analysis, data-mining, k-means
Solution
Don't look for clustering algorithms.
Clustering is a good term for multivariate data, but your data is one-dimensional, so you should look at much older statistics literature. E.g. Natural Breaks optimization.
Or just kernel density estimation. In fact, you will find the very same question dozens of times here on stackoverflow already...
1D Number Array Clustering
Cluster one-dimensional data optimally?
partitioning an float array into similar segments (clustering)
Efficiently grouping similar numbers together
Clustering values by their proximity in python (machine learning?)
Problem
I have a one dimensional List like this ``` public class Zeit_und_Eigenschaft { [Feature] public double Sekunden { get; set; } } //... List<Zeit_und_Eigenschaft> lzue = new List<Zeit_und_Eigenschaft>(); //fill lzue ``` lzue can be ``` lzue.Sekunden 1 2 3 4 8 9 10 22 55 ... ``` Goal is to find clusters in that list, ie elements that could form groups like f.i. in this example ``` lzue.Sekunden 1 2 3 4 8 9 10 22 55 ``` Which clustering algorithm is suitable(I don't know the number of clusters k)? GMM? PCA? Kmeans? Other?