http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Tsui-Ping Chang (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Fast Discovery of Frequent Itemsets through Clustering and Decomposition
Tsui-Ping Chang 한국산학기술학회 2012 SmartCR Vol.2 No.6
In a previous paper, we proposed a technique called the top-down mining (TDM) algorithm to speed up the task of mining hybrid sequential patterns. TDM has a unique feature that examines database itemsets in a top-down manner using decomposition. This method, however, may incur a space problem during decomposition transactions from the database. In this paper, therefore, we propose a new algorithm called transaction decomposition with clustering (TDC) to alleviate the space problem associated with the decomposition method. We use TDC to derive frequent itemsets from a large database. The major feature of TDC is that it divides a database into several smaller projected databases such that each portion can be solved with the decomposition method independently. Since a large amount of information does not have to be stored in memory, the TDC method can efficiently mine frequent itemsets. We compare experimental results for the proposed method and existing algorithms. The results show that TDC can solve the space problem of TDM, and TDC outperforms its counterpart algorithms in many cases. Even when the data set is large or the user-specified minimum support is low, the TDC method still exhibits high performance in mining frequent itemsets. This makes the TDC method suitable for mining frequent itemsets in very large databases.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
