计算机专业科技论文写作必备技巧.ppt

Paper Writing
Paper Writing
Abstract, Keywords
eR Introduction
Related Work (literature Reviewp°。
e< Preliminary
e Algorithm(Method)
Experimental Results
ag Conclusion Future Work
ntroduction
e Background, Topic(1-2 paragraphs
e Literature Review, Motivation(1 paragraph)
8 Algorithm Overview(1 paragraph)°。
eK Contributions(1 paragraphy
e Experiment Overview
eg Roadmap(1 paragraph
Background→ Topic
Background
Time Oriented
Application Oriented
opIC
eK Concept, Characteristics, Application, Importance
and etc
Background→ Topic
FRuodEn pattee to se na dat a mini n9
including association analysis, correlation analysis, caus-
association-based classification and cluster
ing. However, the number of FPs can be too large for them
to be of practical use, especially for dense data sets and/or
when low support thresholds are used. To reduce the
number of FPs, frequent closed pattern(CP) mining has
been introduced and successfully adopted for data analysis
in many domains. In particular, FCPs mined from gene
expression data have been used to build associa tion rules to
uncover gene regulation networks [3], [16 and to build
classifiers for diagnosis [17]
ntroduction
e Background> Topic(1-2 paragraphs
e Literature Review, Motivation(1 paragraph)
XE Algorithm Overview (1 paragrarc
x< Contributions(1 paragraphy
e Experiment Overview
eK Roadmap(1 paragraph)
Literature Review> Motivation
e Notable Method Summarization(Focus on
nearest neighbors
eK Comments(Weakness
e Motivation (against the weakness
Literature Review> Motivation
Some notable FCP mining schemes include CloseT+
[10 CHARM 12, CARPEnTER 6, REPT 3, and D-miner
[2].Although these algorithm
well in their respective context, it turns out that they are not
suited for applications that involve data sets with very high
density, where nearly 50 percent or more of the cells contain
ones (as we shall see, all the real data sets that we used in
the