There is a need to scrutinise and retrieve information from data in today's world. Clustering an analytical technique which involves dividing into groups of similar objects. Every group called cluster, it formed objects that have affinities within the cluster but are significantly different other groups. The aim this paper look at compare two types hierarchical clustering algorithms. Partition ...

Information about the state and planning of the speaker is obscured in traditional classifications of disfluencies which are generally at the word level. This study delves into the acoustic and prosodic information of repetitions, one of the most common disfluencies. A hierarchical clustering of prosodic features reveals three subsets of repetitions, each reflecting different problems in planning.

Utilizing context information—e.g. location, user aspects, or hardware capabilities—enables the presented generic framework to automatically control the selection and configuration of visualization techniques and therewith provide interactive illustrations, displayed on small mobile devices. For contextannotated data, provided by an underlying context-aware world model, the proposed system dete...

We create a weighted lexical network derived from the cosine similarities of financial news feeds to compare two clustering methods, Newman's Modularity method and hierarchical clustering. We find that hierarchical clustering, clustering documents according to shared unique terms, shows results that are closer to expectation.