Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 89-93).

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 138-144). Also available in electronic version. Access restricted to campus users.

In today's world, desktop computers have become such an integral part of our lives that it is practically impossible to imagine anything being done without the aid of computers. As the world becomes more and more fast paced and users feel a need to have computers on the go, desktop computers have reduced in size without compromising on performance. The late 90s saw the desktop segment make room for the laptop and the small screen devices (SSD) segment, which demonstrated faster growth rates than the desktop segment. The SSD segment, however, had a growth rate that was nowhere near the combined growth rate of desktop and laptop computers. Portability of SSD was one factor that stood out among many others to account for the unprecedented growth rate of the SSD segment that the computer industry had witnessed. One of the most important, albeit under-represented and neglected, factors of a product is its usability. Usability, or the ease with which a product can be used, can be considered to be one of the most important factors in the success or failure of product. Determining the usability of small screen devices presents a bigger challenge, primarily because of the screen size of the SSD. The process of usability engineering aims to solve some/most of the problems that the SSD has. To make up for the drawbacks of usability engineering, systems engineering was used in this thesis, since both disciplines have considerable overlap in their processes. A growing number of SSD users use the Internet in one form or the other. The Internet has grown rapidly in the last decade, and nearly everyone using the Internet has come across a search engine sometime or other. Although research has been limited to the area of desktop search engines, there has not been enough research done in the area of search engines for small screen devices. This thesis compares two different search engines on small screen devices to find the better between the two. To do so, it takes a close look at the usability engineering approach from a system engineering perspective revealing several deficiencies, which may have hitherto gone unnoticed. It also shows a method to integrate several key Systems Engineering components into the usability engineering approach. / Graduation date: 2006

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

Thesis (M. Phil.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 78-81).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 68-73). Also available in electronic version.

Thesis (Ph.D.)--Brown University, 2005. / Vita. Thesis advisor: Thomas L. Dean. Includes bibliographical references (leaves 185-191). Also available online.

by Wong, Wai-Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 89-94). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Document Clustering --- p.2 / Chapter 1.2 --- DC-tree --- p.4 / Chapter 1.3 --- Feature Extraction --- p.5 / Chapter 1.4 --- Outline of the Thesis --- p.5 / Chapter 2 --- Related Work --- p.8 / Chapter 2.1 --- Clustering Algorithms --- p.8 / Chapter 2.1.1 --- Partitional Clustering Algorithms --- p.8 / Chapter 2.1.2 --- Hierarchical Clustering Algorithms --- p.10 / Chapter 2.2 --- Document Classification by Examples --- p.11 / Chapter 2.2.1 --- k-NN algorithm - Expert Network (ExpNet) --- p.11 / Chapter 2.2.2 --- Learning Linear Text Classifier --- p.12 / Chapter 2.2.3 --- Generalized Instance Set (GIS) algorithm --- p.12 / Chapter 2.3 --- Document Clustering --- p.13 / Chapter 2.3.1 --- B+-tree-based Document Clustering --- p.13 / Chapter 2.3.2 --- Suffix Tree Clustering --- p.14 / Chapter 2.3.3 --- Association Rule Hypergraph Partitioning Algorithm --- p.15 / Chapter 2.3.4 --- Principal Component Divisive Partitioning --- p.17 / Chapter 2.4 --- Projections for Efficient Document Clustering --- p.18 / Chapter 3 --- Background --- p.21 / Chapter 3.1 --- Document Preprocessing --- p.21 / Chapter 3.1.1 --- Elimination of Stopwords --- p.22 / Chapter 3.1.2 --- Stemming Technique --- p.22 / Chapter 3.2 --- Problem Modeling --- p.23 / Chapter 3.2.1 --- Basic Concepts --- p.23 / Chapter 3.2.2 --- Vector Model --- p.24 / Chapter 3.3 --- Feature Selection Scheme --- p.25 / Chapter 3.4 --- Similarity Model --- p.27 / Chapter 3.5 --- Evaluation Techniques --- p.29 / Chapter 4 --- Feature Extraction and Weighting --- p.31 / Chapter 4.1 --- Statistical Analysis of the Words in the Web Domain --- p.31 / Chapter 4.2 --- Zipf's Law --- p.33 / Chapter 4.3 --- Traditional Methods --- p.36 / Chapter 4.4 --- The Proposed Method --- p.38 / Chapter 4.5 --- Experimental Results --- p.40 / Chapter 4.5.1 --- Synthetic Data Generation --- p.40 / Chapter 4.5.2 --- Real Data Source --- p.41 / Chapter 4.5.3 --- Coverage --- p.41 / Chapter 4.5.4 --- Clustering Quality --- p.43 / Chapter 4.5.5 --- Binary Weight vs Numerical Weight --- p.45 / Chapter 5 --- Web Document Clustering Using DC-tree --- p.48 / Chapter 5.1 --- Document Representation --- p.48 / Chapter 5.2 --- Document Cluster (DC) --- p.49 / Chapter 5.3 --- DC-tree --- p.52 / Chapter 5.3.1 --- Tree Definition --- p.52 / Chapter 5.3.2 --- Insertion --- p.54 / Chapter 5.3.3 --- Node Splitting --- p.55 / Chapter 5.3.4 --- Deletion and Node Merging --- p.56 / Chapter 5.4 --- The Overall Strategy --- p.57 / Chapter 5.4.1 --- Preprocessing --- p.57 / Chapter 5.4.2 --- Building DC-tree --- p.59 / Chapter 5.4.3 --- Identifying the Interesting Clusters --- p.60 / Chapter 5.5 --- Experimental Results --- p.61 / Chapter 5.5.1 --- Alternative Similarity Measurement : Synthetic Data --- p.61 / Chapter 5.5.2 --- DC-tree Characteristics : Synthetic Data --- p.63 / Chapter 5.5.3 --- Compare DC-tree and B+-tree: Synthetic Data --- p.64 / Chapter 5.5.4 --- Compare DC-tree and B+-tree: Real Data --- p.66 / Chapter 5.5.5 --- Varying the Number of Features : Synthetic Data --- p.67 / Chapter 5.5.6 --- Non-Correlated Topic Web Page Collection: Real Data --- p.69 / Chapter 5.5.7 --- Correlated Topic Web Page Collection: Real Data --- p.71 / Chapter 5.5.8 --- Incremental updates on Real Data Set --- p.72 / Chapter 5.5.9 --- Comparison with the other clustering algorithms --- p.73 / Chapter 6 --- Conclusion --- p.75 / Appendix --- p.77 / Chapter A --- Stopword List --- p.77 / Chapter B --- Porter's Stemming Algorithm --- p.81 / Chapter C --- Insertion Algorithm --- p.83 / Chapter D --- Node Splitting Algorithm --- p.85 / Chapter E --- Features Extracted in Experiment 4.53 --- p.87 / Bibliography --- p.88

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2007. / Includes bibliographical references (leaves 98-113). Also available online.

Thesis (M.S.)--State University of New York at Binghamton, Watson School of Engineering and Applied Science (Computer Science), 2005. / Includes bibliographical references.