#### Graduation Year

2003

#### Document Type

Dissertation

#### Degree

Ph.D.

#### Degree Granting Department

Computer Science and Engineering

#### Major Professor

Abraham Kandel, Ph.D.

#### Committee Member

Dewey Rundus, Ph.D.

#### Committee Member

Horst Bunke, Ph.D.

#### Committee Member

Ken Christensen, Ph.D.

#### Committee Member

Carlos Smith, Ph.D.

#### Keywords

graph similarity, graph distance, machine learning, clustering, classification

#### Abstract

In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied to data represented by graphs since no suitable graph-theoretical concepts were previously available.

We introduce the novel Graph Hierarchy Construction Algorithm (GHCA), which performs topic-oriented hierarchical clustering of web search results modeled using graphs. The system we created around this new algorithm and its prior version is compared with similar web search clustering systems to gauge its usefulness. An important advantage of this approach over conventional web search systems is that the results are better organized and more easily browsed by users.

Next we present extensions to classical machine learning algorithms, such as the *k*-means clustering algorithm and the *k*-Nearest Neighbors classification algorithm, which allows the use of graphs as fundamental data items instead of vectors. We perform experiments comparing the performance of the new graph-based methods to the traditional vector-based methods for three web document collections. Our experimental results show an improvement for the graph approaches over the vector approaches for both clustering and classification of web documents. An important advantage of the graph representations we propose is that they allow the computation of graph similarity in polynomial time; usually the determination of graph similarity with the techniques we use is an NP-Complete problem. In fact, there are some cases where the execution time of the graph-oriented approach was faster than the vector approaches.

#### Scholar Commons Citation

Schenker, Adam, "Graph-Theoretic Techniques for Web Content Mining" (2003). *Graduate Theses and Dissertations.*

https://digitalcommons.usf.edu/etd/1467