지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.5
- 수록면
- 297 - 303 (7page)
이용수
초록· 키워드
오류제보하기
A stream data is a data set that is accumulated to the data storage from a data source over time continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes much resource such as storage, memory and time. These unique characteristics of the stream data make it difficult and expensive to use this large size data accumulated over time. Otherwise, if we use only recent or part of a whole data to mine information or pattern, there can be loss of information, which may be useful. To avoid this problem, we suggest a method that efficiently accumulates information, in the form of rule sets, over time. It takes much smaller storage compared to traditional mining methods. This accumulated rule set is used as a prediction model in future. Based on theories of ensemble approaches, combination of many prediction models, in the form of systematically merged rule sets in this study, is better than one prediction model in performance. This study uses a stock market data set that stock index in Korea.
목차
Abstract
I. 서론
II. 기존연구
III. 연구모형
IV. 주식 시장 자료에의 적용
V.결론
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UCI(KEPA) : I410-ECN-0101-2015-003-001491878