Mining Frequent Induced Subtrees by Prefix-Tree-Projected Pattern Growth

2006 ◽  
Author(s):  
Lei Zou ◽  
Yansheng Lu ◽  
Huaming Zhang ◽  
Rong Hu ◽  
Chong Zhou
Keyword(s):  
Prefix Tree ◽  
Pattern Growth

scholarly journals CMOS based Power Efficient Digital Comparator with Parallel Prefix Tree Structure

2018 ◽  
Vol 7 (2.7) ◽  
pp. 647
Author(s):  
J Lakshmi Prasanna ◽  
V Sahiti ◽  
E Raghuveera ◽  
M Ravi Kumar
Keyword(s):  
Complementary Metal Oxide Semiconductor ◽  
Logic Gates ◽  
Tree Structure ◽  
Oxide Semiconductor ◽  
Least Significant Bit ◽  
Power Efficient ◽  
Prefix Tree ◽  
Digital Comparator ◽  
Parallel Prefix ◽  
Transmission Gates

A 128-Bit Digital Comparator is designed with Digital Complementary Metal Oxide Semiconductor (CMOS) logic, with the use of Parallel Prefix Tree Structure [1] technique. The comparison is performed on Most Significant Bit (MSB) to the Least Significant Bit (LSB). The comparison for the lower order bits carried out only when the MSBs are equal. This technique results in Optimized Power consumption and improved speed of operation. To make the circuit regular, the design is made using only CMOS logic gates. Transmission gates were used in the existing design and are replaced with the simple AND gates. This 128-Bit comparator is designed using Cadence TSMC 0.18µm technology and optimized the Power dissipation to 0.28mW and with a Delay of 0.87μs. 

scholarly journals SPADE: Activity Prediction in Smart Homes Using Prefix Tree Based Context Generation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 5492-5501 ◽  
Author(s):  
Araf Farayez ◽  
Mamun Bin Ibne Reaz ◽  
Norhana Arsad
Keyword(s):  
Smart Homes ◽  
Activity Prediction ◽  
Prefix Tree

A MALAY TEXT CORPUS ANALYSIS FOR SENTENCE COMPRESSION USING PATTERN-GROWTH METHOD

2016 ◽  
Vol 78 (8) ◽  
Author(s):  
Suraya Alias ◽  
Siti Khaotijah Mohammad ◽  
Gan Keng Hoon ◽  
Tan Tien Ping
Keyword(s):  
Text Summarization ◽  
Corpus Analysis ◽  
Text Corpus ◽  
Sentence Compression ◽  
Condensed Representation ◽  
Pattern Growth ◽  
Growth Method ◽  
Document Collection ◽  
Input Source ◽  
Confidence Value

A text summary extracts serves as a condensed representation of a written input source where important and salient information is kept. However, the condensed representation itself suffer in lack of semantic and coherence if the summary was produced in verbatim using the input itself. Sentence Compression is a technique where unimportant details from a sentence are eliminated by preserving the sentence’s grammar pattern. In this study, we conducted an analysis on our developed Malay Text Corpus to discover the rules and pattern on how human summarizer compresses and eliminates unimportant constituent to construct a summary. A Pattern-Growth based model named Frequent Eliminated Pattern (FASPe) is introduced to represent the text using a set of sequence adjacent words that is frequently being eliminated across the document collection. From the rules obtained, some heuristic knowledge in Sentence Compression is presented with confidence value as high as 85% - that can be used for further reference in the area of Text Summarization for Malay language.

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