An Update Algorithm for Restricted Random Walk Clusters

This book presents the dynamic extension of the Restricted Random Walk Cluster Algorithm by Schöll and Schöll-Paschinger. The dynamic variant allows to quickly integrate changes in the underlying object set or the similarity matrix into the clusters; the results are indistinguishable from the renewed execution of the original algorithm on the updated data set

Graph based pattern discovery in protein structures

The rapidly growing body of 3D protein structure data provides new opportunities to study the relation between protein structure and protein function. Local structure pattern of proteins has been the focus of recent efforts to link structural features found in proteins to protein function. In addition, structure patterns have demonstrated values in applications such as predicting protein-protein interaction, engineering proteins, and designing novel medicines. My thesis introduces graph-based representations of protein structure and new subgraph mining algorithms to identify recurring structure patterns common to a set of proteins. These techniques enable families of proteins exhibiting similar function to be analyzed for structural similarity. Previous approaches to protein local structure pattern discovery operate in a pairwise fashion and have prohibitive computational cost when scaled to families of proteins. The graph mining strategy is robust in the face of errors in the structure, and errors in the set of proteins thought to share a function. Two collaborations with domain experts at the UNC School of Pharmacy and the UNC Medical School demonstrate the utility of these techniques. The first is to predict the function of several newly characterized protein structures. The second is to identify conserved structural features in evolutionarily related proteins

Equivalence of Transforming Non-Linear DACG to Linear Concept Tree

This paper introduces a strategy and its theory proof to transform non-linear concept graph: Directed Acyclic Concept Graph (DACG) into a linear concept tree. The transformation is divided into three steps: normalizing DACG into a linear concept tree, establishing a function on host attribute, and reorganizing the sequence of concept generalization. This study develops alternative approach to discovery knowledge under non-linear concept graph. It overcomes the problems with information loss in rule-based attribute oriented induction and low efficiency in path-id method. Because DACG is a more general concept schema, it is able to extract rich knowledge implied in different directions of non-linear concept scheme