by Cheung Wai-Hung Wellis. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 52-54). / ACKNOWLEDGEMENTS / ABSTRACT / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- CLASSIFICATION OF CHARACTER RECOGNITION --- p.1 / Chapter 1.2 --- HISTORICAL DEVELOPMENT --- p.3 / Chapter 1.3 --- RECOGNITION METHODOLOGY --- p.4 / Chapter 2 --- ORGANIZATION OF THIS REPORT --- p.7 / Chapter 3 --- DATA SAMPLING --- p.8 / Chapter 3.1 --- GENERAL CONSIDERATION --- p.8 / Chapter 3.2 --- IMPLEMENTATION --- p.9 / Chapter 4 --- PREPROCESSING --- p.10 / Chapter 4.1 --- GENERAL CONSIDERATION --- p.10 / Chapter 4.2 --- IMPLEMENTATION --- p.12 / Chapter 4.2.1 --- Stroke connection --- p.12 / Chapter 4.2.2 --- Rotation --- p.12 / Chapter 4.2.3 --- Scaling --- p.14 / Chapter 4.2.4 --- De-skewing --- p.15 / Chapter 5 --- STROKE SEGMENTATION --- p.17 / Chapter 5.1 --- CONSIDERATION --- p.17 / Chapter 5.2 --- IMPLEMENTATION --- p.20 / Chapter 6 --- LEARNING --- p.26 / Chapter 7 --- PROTOTYPE MANAGEMENT --- p.27 / Chapter 8 --- RECOGNITION --- p.29 / Chapter 8.1 --- CONSIDERATION --- p.29 / Chapter 8.1.1 --- Delayed Stroke Tagging --- p.29 / Chapter 8.1.2 --- Bi-gram --- p.29 / Chapter 8.1.3 --- Character Scoring --- p.30 / Chapter 8.1.4 --- Ligature Handling --- p.32 / Chapter 8.1.5 --- Word Scoring --- p.32 / Chapter 8.2 --- IMPLEMENTATION --- p.33 / Chapter 8.2.1 --- Simple Matching --- p.33 / Chapter 8.2.2 --- Best First Search Matching --- p.33 / Chapter 8.2.3 --- Multiple Track Method --- p.35 / Chapter 8.3 --- SYSTEM PERFORMANCE TUNING --- p.37 / Chapter 9 --- POST-PROCESSING --- p.38 / Chapter 9.1 --- PROBABILITY MODEL --- p.38 / Chapter 9.2 --- WORD DICTIONARY APPROACH --- p.39 / Chapter 10 --- SYSTEM IMPLEMENTATION AND PERFORMANCE --- p.41 / Chapter 11 --- DISCUSSION --- p.43 / Chapter 12 --- EPILOG --- p.47 / Chapter APPENDIX I - --- PROBLEMS ENCOUNTERED AND SUGGESTED ENHANCEMENTS ON THE SYSTEM --- p.48 / Chapter APPENDIX II - --- GLOSSARIES --- p.51 / REFERENCES --- p.52

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Online mining sequential patterns over data streams is an important problem in data mining. There are many applications of using sequential patterns in data streams, such as market analysis, network security, sensor networks and web track- ing. Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to compact results. A sequential pattern is closed if the sequential pattern does not have any supersequence which has the same support. Chang et al. proposed a time- based sliding window model. The time-based sliding window has two features, the new item is inserted in front of a sequence, and the obsolete item is removed from of tail of a sequence. For solving the problem of data mining in the time-based sliding window, Chang et al. proposed an algorithm called SeqStream. It uses a data struc- ture IST (Inverse Closed Sequence Tree) to keep the result. IST can incrementally be updated by the SeqStream algorithm. Although the SeqStream algorithm has used the technique of dividing the time-based sliding window to speed up the updating of IST, the SeqStream algorithm still scans the sliding window many times when IST needs to be updated. In this thesis, we propose an obstruction-check approach to maintain the result of closed sequential patterns. Our approach is designed based on the lattice structure. The feature of the lattice structure is that the parent is a supersequence of its children. By utilizing this feature, we decide the obstruction link between the parent and child if their support is the same. If a node does not have any obstruction link parent, the node is a closed sequential pattern. Then we can utilize this feature to locally travel the lattice structure. Moreover, we can fully utilize the features of the time-based sliding window model to locally travel the lat- tice structure. Based on the lattice structure, we propose the EULB (Exact Update based on Lattice structure with Bit stream)-Lattice algorithm. The EULB-Lattice algorithm is an exact method for mining data streams. We record additional informa- tion, instead of scanning the entire sliding window. We conduct several experiments using different synthetic data sets. The simulation results show that the proposed algorithm outperforms the SeqStream algorithm.

In recent years, with the popularization of the mobile devices, more and more location-based applications have been developed. As a result, location data of various objects is widely available. Identifying object groups that tend to move together is an emerging research topic. Existing approaches for identifying mobile group patterns assume the existence of raw location data which records a given object¡¦s position at every equal-spaced time point. However, a moving object may become disconnected voluntarily or involuntarily from time to time, and thus this assumption may not always valid. In this research, we describe the locations of moving object as a (non-continuous) trajectory function. Based on the new model, we re-define the mobile group mining problem and develop efficient algorithms for mining mobile groups. The proposed algorithms are evaluated via synthetic data generated by IBM City Simulator.

With the rapidly growing volumes of data from various sources, new tools and computational theories are required to extract useful information (knowledge) from large databases. Data mining techniques such as association rules have been proved to be effective in searching hidden knowledge in a large database. However, if we want to extract knowledge from data with temporal components, it becomes necessary to incorporate temporal semantics with the traditional data mining techniques. As mining techniques evolves, mathematical techniques become more involved to help improve the quality and diversity of mining. Fuzzy theory is one that has been adopted for this purpose. Up to now, many approaches have been proposed to discover temporal association rules or fuzzy association rules, respectively. However, no work is contributed on mining fuzzy temporal patterns. We propose in this thesis two data mining systems for discovering fuzzy temporal association rules and fuzzy periodic association rules, respectively. The mined patterns are expressed in fuzzy temporal and periodic association rules which satisfy the temporal requirements specified by the user. Temporal requirements specified by human beings tend to be ill-defined or uncertain. To deal with this kind of uncertainty, a fuzzy calendar algebra is developed to allow users to describe desired temporal requirements in fuzzy calendars easily and naturally. Moreover, the fuzzy calendar algebra helps the construction of desired time intervals in which interesting patterns are discovered and presented in terms of fuzzy temporal and periodic association rules. In our system of mining fuzzy temporal association rules, a border-based mining algorithm is proposed to find association rules incrementally. By keeping useful information of the database in a border, candidate itemsets can be computed in an efficient way. Updating of the discovered knowledge due to addition and deletion of transactions can also be done efficiently. The kept information can be used to help save the work of counting and unnecessary scans over the updated database can be avoided. Simulation results show the effectiveness of the proposed system for mining fuzzy temporal association rules. In our mining system for discovering fuzzy periodic association rules, we develop techniques for discovering patterns with periodicity. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicites in the database are usually not very precise and have disturbances, and might occur at time intervals in multiple time granularities. To discover the patterns with fuzzy periodicity, we utilize the information of crisp periodic patterns to obtain a lower bound for generating candidate itemsets with fuzzy periodicities. Experimental results have shown that our system is effective in discovering fuzzy periodic association rules.

In 2003, Johannes Kellendonk and Ian Putnam introduced pattern equivariant cohomology for tilings. In 2006, Betseygail Rand defined a type of pattern equivariant cohomology that incorporates rotational symmetry, using representation of the rotation group. In this doctoral thesis we study the relationship between these two types of pattern equivariant cohomology, showing exactly how to calculate one from the other in the case in which the rotation group is a finitely generated abelian group of free rank 1. We apply our result by calculating the cohomology of the pinwheel tiling.

Thesis (Ph. D.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 132-137). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 107-114). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--University of California, Santa Cruz, 1988. / Typescript. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references (p.137-141). Also available online,