By Ho Thi Huong Thom, Ho Van Canh, Trinh Nhat Tien (auth.), Dominik Ślęzak, Tai-hoon Kim, Yanchun Zhang, Jianhua Ma, Kyo-il Chung (eds.)
As destiny iteration details know-how (FGIT) turns into really good and fr- mented, you'll lose sight that many issues in FGIT have universal threads and, due to this, advances in a single self-discipline could be transmitted to others. Presentation of contemporary effects received in several disciplines encourages this interchange for the development of FGIT as an entire. Of specific curiosity are hybrid strategies that c- bine principles taken from a number of disciplines on the way to in achieving anything extra signi- cant than the sum of the person components. via such hybrid philosophy, a brand new precept may be found, which has the propensity to propagate all through mul- faceted disciplines. FGIT 2009 used to be the 1st mega-conference that tried to persist with the above thought of hybridization in FGIT in a kind of a number of occasions regarding specific disciplines of IT, performed by means of separate medical committees, yet coordinated so as to disclose crucial contributions. It integrated the next overseas meetings: complicated software program Engineering and Its purposes (ASEA), Bio-Science and Bio-Technology (BSBT), keep watch over and Automation (CA), Database idea and alertness (DTA), D- aster restoration and enterprise Continuity (DRBC; released independently), destiny G- eration conversation and Networking (FGCN) that used to be mixed with complex communique and Networking (ACN), Grid and dispensed Computing (GDC), M- timedia, special effects and Broadcasting (MulGraB), defense expertise (SecTech), sign Processing, picture Processing and trend attractiveness (SIP), and- and e-Service, technology and expertise (UNESST).
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Additional info for Database Theory and Application: International Conference, DTA 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, Jeju Island, Korea, December 10-12, 2009. Proceedings
An Investigation of Linguistic Features and Clustering Algorithms for Topical Document Clustering. In: The Proceedings of 23rd SIGIR, pp. 224–231 (2000) 5. : A K-Means Clustering Algorithm. Applied Statistics 28(1), 101–108 (1979) 6. : Frequent term-based text clustering. In: The Proceedings of the eighth ACM SIGKDD international conference on Knowl- edge discovery and data mining, pp. 436–442 (1994) 7. : Fast and Eﬀective Text Mining Using Linear-time Doc- ument Clustering. In: The Proceedings of the ﬁfth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.
In this paper, we resent an improved algorithm MAFIM (a maximal approximate frequent itemsets mining) for frequent itemsets mining based on approximate counting using previous saved maximal frequent itemsets. Chernoff bound based algorithm is given, that guarantee of the output quality and also a bound on the memory usage. Therefore, our algorithm is controlled by two parameters ε and δ for error bounds and reliability. We can see a reasonable value for ε, accurate result, fast computation and low memory utilization can be achieved.
The sample labeled documents should be arranged and each of them should be labeled again with the positive or negative class. In order to save the cost for the decomposition, we attempt to adopt and apply the NTC (Neural Text Categorizer) in this research. If the feasibility is guaranteed, there are advantages in applying a machine learning algorithm to the text categorization without the decomposition. The ﬁrst advantage is that we need not to relabel the given sample labeled documents. The second advantage is that only a classiﬁer is enough for carrying out the entire text categorization.