• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.519-531
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• 2020

Global warming is accelerating as greenhouse gas emissions increase owing to the increase in population and urbanization rates worldwide. As an alternative to this solution, smart cities are being promoted. The purpose of this paper is to suggest a plan for developing energy service modules for the Sejong 5-1 living area, which has been selected as a test-bed for smart cities in Korea. Based on the smart city plans announced by the government for this study, a survey questionnaire on 12 energy services was composed by collecting the opinions of experts. The survey was conducted with 1,000 citizens, the degree of necessity of energy service that citizens think of was identified. Principal Component Analysis and Association Rule Mining were conducted to describe 12 energy service items in a reduced manner and analyze the correlation and relationship of each energy service. Finally, three modules were suggested using the analyzed results so that 12 energy services could be implemented in an efficient platform. These results are expected to contribute to the realization of a smart city to make them easily accessible for those who want to promote platform services in the energy field and envision energy service items.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.279-300
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• 2001

Data mining plays an important role in knowledge discovery process and usually various existing algorithms are selected for the specific purpose of the mining. Currently, data mining techniques are actively to the statistics, business, electronic commerce, biology, and medical area and currently numerous algorithms are being researched and developed for these applications. However, in a long run, only a few algorithms, which are well-suited to specific applications with excellent performance in large database, will survive. So it is reasonable to focus our effort on those selected algorithms in the future. This paper classifies about 30 existing algorithms into 7 categories - association rule, clustering, neural network, decision tree, genetic algorithm, memory-based reasoning, and bayesian network. First of all, this work analyzes systematic hierarchy and characteristics of algorithms and we present 14 criteria for classifying the algorithms and the results based on this criteria. Finally, we propose the best algorithms among some comparable algorithms with different features and performances. The result of this paper can be used as a guideline for data mining researches as well as field applications of data mining.