Seeking for a fast non parametic clustering algorithm I'm looking for a fast clustering method to cluster a large kind of datas to a unknown count of clusters.
I know about the PAM-Algorithm. But it's only efficient for low datasets.
Is there a alternative Algorithm? (And maybe a implementation in C or Java?)
//EDIT
With "parameter-free" i would said that i dont define the number of clusters or the distance between them. 
In time i only want to cluster time-stamps with the simple L1-Distance(maybe datasets between 1.000 and 5.000 datas). But maybe i want to be cluster datas in NxM (I'm not sure).
greetings
 A: If you can recast your data as a network, then mcl clustering could work - it can handle millions of nodes. It has just one parameter that affects the granularity of the resulting clustering: it is possible to find clusterings at different levels of granularity, but not to specify the number of clusters. Disclaimer: I wrote it. It is used a lot in the field of bioinformatics.
High dimensional data can be cast to a network by using a set intersect similarity such as tanimoto similarity, or by a correlation coefficient (which is a type of similarity) such as Pearson's or Spearman's. Most network clustering algorithms expect such a similarity rather than a distance.
A: What did you try so far? DBSCAN has two parameters that can usually be set by a domain expert. OPTICS only has one of them, which is roughly a "minimum cluster size". And there must be 1000 other clustering algorithms.
Usually, algorithms that claim to be "parameter free" either just lie to you (parameters are hidden in form of distance function, data normalization, preprocessing ...) or just don't work that well. Sometimes they just don't offer you choice to get the label "parameter free".
Clustering is exploration. You can't explore if it's a single-shot thing. You will need multiple runs to really learn something new about your data. A single-shot approach is overfitted to tell you the obvious things (which you probably already know).
