On finding the number of clusters

In this work a novel hierarchical clustering technique is proposed which can be used to find a hierarchical structure of data items in a dataset. The emphasis is on maintaining consistency of the clusters at each level in the hierarchy as well as ...

• Algorithm to find the hierarchical structure of clusters.
• Automatically detect the number of levels and the number of clusters at each level.
• Automatically detect association between clusters at adjacent levels.
• Can handle ...

In this paper, we present an agglomerative fuzzy $k$-means clustering algorithm for numerical data, an extension to the standard fuzzy $k$-means algorithm by introducing a penalty term to the objective function to make the clustering process not ...

Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very ...