The purpose of this study is to analyze consumer behavior in bride cake market. The study divides into two parts: one aims consumers who married before 1996 and are above 40-year-old. Through deep interviewing, we can profile consumer behavior in the bribe cake before 1996. The sample of the second study is singles of 18 to 35 years old. According to decision process of EKB model, this research tries to realize consumer purchasing behavior. The results of this research are as follows: 1. The scale of bride cake industry won¡¦t diminish at the present time. 2. Acceptable price is rising up. 3. Western bride cake is consumer¡¦s favorite, then goes to the mixed form. 4. Famous bride cake chain store is the most favorite buying place. 5. Credit is the favorite payment pattern. 6. Valuable information comes from relatives, friends, and store¡¦s sales. 7. In terms of product attributes, consumer emphasizes ¡§good taste¡¨, ¡§good services¡¨, and ¡§fair price¡¨. 8. Geographical variables don¡¦t have significant differences in consumer behavior. 9. Via cluster analysis, samples are divided into three groups, namely ¡§Fashion Seeker¡¨, ¡§Innovator and Adventurer¡¨, and ¡§Realist and economist¡¨. In terms of demographic variables, significant differences exist only in sex and age. There are more female in ¡§Fashion Seeker¡¨ and ¡§Realist and economist¡¨. And the age in these two groups is for the most part range from 18 to 25 years old. The age of ¡§Innovator and Adventurer¡¨ is for the most part range from 26 to 30 years old. ¡§Fashion Seeker¡¨ values ¡§vogue¡¨, ¡§beautiful looking¡¨ and ¡§notable brand¡¨, and concerned ¡§TV advertisement¡¨.

This dissertation presents the configuration and capacity management of manufacturing cells using cluster analysis. A heuristic based on cluster analysis is developed to solve cell formation in cellular manufacturing systems (CMS). The clustering heuristic is applied for cell formation considering processing requirement (CFOPR) as well as various manufacturing factors (CFVMF). The proposed clustering heuristic is developed by employing a new solving structure incorporating hierarchical and non-hierarchical clustering methods. A new similarity measure is constructed by modifying the Jarccard similarity and a new assignment algorithm is proposed by employing the new pairwise exchange method. In CFOPR, the clustering heuristic is modified by adding a feedback step and more exact allocation rules. Grouping efficacy is employed as a measure to evaluate solutions obtained from the heuristic. The clustering heuristic for CFOPR was evaluated on 23 test problems taken from the literature in order to compare with other approaches and produced the best solution in 18 out of 23 and the second best in the remaining problems. These solutions were obtained in a considerably short time and even the largest test problem was solved in around one and a half seconds. In CFVMF, the machine capacity was first ensured, and then manufacturing cells were configured to minimize intercellular movements. In order to ensure the machine capacity, the duplication of machines and the split of operations are allowed and operations are assigned into duplicated machines by the largest-first rule. The clustering heuristic for CFVMF proposes a new similarity measure incorporating processing requirement, material flow and machine workload and a new machine-part matrix representing material flow and processing time assigned to multiple identical machines. Also, setup time, which has not been clearly addressed in existing research, is discussed in the solving procedure. The clustering heuristic for CFVMF employs two evaluation measures such as the number of intercellular movements and grouping efficacy. In two test problems taken from the literature, the heuristic for CFVMF produced the same results, but the trade-off problem between the two evaluation measures is proposed to consider the goodness of grouping.

Thesis (Ph. D.)--University of Cincinnati, 2006. / Includes bibliographical references (leaf 142-147). Also available online.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Statistics and Biostatistics." Includes bibliographical references (p. 75-78).

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 87-90). Also available in print.

Includes bibliographical references (p. 103-111).

With the advancement of Internet technology and the changes in the mode of communications, it is found that much first-hand news have been discussed in Internet forums well before they are reported in traditional mass media. Also, this communication channel provides an effective channel for illegal activities such as dissemination of copyrighted movies, threatening messages and online gambling etc. The law enforcement agencies are looking for solutions to monitor these discussion forums for possible criminal activities and download suspected postings as evidence for investigation. The volume of postings is huge, for 10 popular forums in Hong Kong; we found that there are 300,000 new messages every day. In this thesis, we propose an automatic system that tackles this problem. Our proposed system downloads postings from selected discussion forums continuously and employs data mining techniques to identify hot topics and cluster authors into different groups using word based user profiles. Using these data, we try to locate some useful trends and detect crime from the data, the result is discussed afterward with include advantages and limitations of different approaches and at the end, there is a conclusion of the way to solve those problems and provide future direction of this research. / published_or_final_version / Computer Science / Master / Master of Philosophy

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