Scalable adaptive hierarchical clustering

Finally, the C model simplifies the graph by removing both join and split nodes: since JOIN-XOR and SPLIT-XOR operators represent different alternative executable

In the first part, we study the problem of scalable clustering algorithm based on Mean Shift algorithm for continuous features.. We propose a new design for the Mean Shift

A memory control mecha- nism is applied to the link hierarchy during the modified refine/gather/push-pull: based on user defined memory limits, links are established at higher levels

For example in Figure 3 the obtained super-clusters are tiny-pores, small-pores, big-pores1 and big-pores2 (these names have been conventionally assigned by domain experts after

Complete author clustering: We do a detailed analysis, where we need to identify the number k of different authors (clusters) in a collection and assign each docu- ment to exactly

Initially, we applied the H-FCM algorithm (with m=1.1) to the test document collections for a number of clusters that matched the number of reference classes c REF and calculated

Apart from verifying cluster hypothesis, other tests are carried out to compare retrieval effectiveness using different clustering techniques, among which, four hie-

Other variants tried to allow this fuzzy clustering in the hierarchy such as SOHC [10], a hierarchical clustering based on a spatial overlapping metric but with a fixed number