• Journal of the Korean Institute of Landscape Architecture International Edition
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• no.2
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• pp.216-220
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• 2004

Many efforts have been made to improve the management of large-scale green spaces. How to manage large-scale green spaces and their active uses, as well as how to build relationships with local communities have been important issues. For this research, I reviewed the actual status of management, use and citizen participation at large-scale regional parks in Hyogo Prefecture. In addition, I studied the sustainable management through citizen participation of the Awaji Island Regional Park, where I have been involved for several years. I conducted various projects related to the use and management of the park, and examined the direction of citizen participation by conducting questionnaires and interviews. (1) Through interviews about the park, I collected opinions, including good points, problems, and potential solutions through physical and programming measures. (2) I examined what kinds of activities should be conducted in the park in order to revitalize park use and stimulate the surrounding communities. (3) I examined the current status of citizen participation while citizens carried out activities of their own planning. (4) I studied what is necessary to sustain park events and other activities. As a result, I came to the following conclusions. (1) Provision of information that is easy to access, including signs in the park, explanation of routes in large parks, and other techniques that help people become familiar with park facilities, is very important. (2) Local community events, and programs that draw out the willingness and capabilities of volunteers are effective. (3) Several different types of participation exist, including volunteers, guests, staff who work continuously for the project, coordinators, and professional specialists. (4) To sustain citizen involvement in the use and management of large-scale parks, a system that includes coordinators should be developed.

• Fashion Information and Technology
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• v.5
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• pp.64-72
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• 2008

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.857-864
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• 2015

By Wikipedia, data mining is the computational process of discovering patterns in huge data sets involving methods at the intersection of association rule, decision tree, clustering, artificial intelligence, machine learning. Clustering or cluster analysis is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. The similarity measures being used in the clustering may be classified into various types depending on the characteristics of data. In this paper, we computed bounds for similarity measures based on the probabilistic interestingness measure with partially marginal probability such as Peirce I, Peirce II, Cole I, Cole II, Loevinger, Park I, and Park II measure. We confirmed the absolute value of Loevinger measure wasthe upper limit of the absolute value of any other existing measures. Ordering of other measures is determined by the size of concurrence proportion, non-simultaneous occurrence proportion, and mismatch proportion.