Online Structural Graph Clustering Using Frequent Subgraph Mining
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or the size of the maximum common subgraph. This can serve to structure the graph space and to improve the understanding of the data. In this paper, we present […]
Data clustering-50 years beyond K-means
Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to […]
On the Comparability of Software Clustering Algorithms
Evaluation of software clustering algorithms is typically done by comparing the clustering results to an authoritative decomposition prepared manually by a system expert. A well-known drawback of this approach is the fact that there are many, equally valid ways to decompose a software system, since different clustering objectives create different decompositions. Evaluating all clustering algorithms […]