PrefixTreeESpan: A Pattern Growth Algorithm for Mining Embedded Subtrees

2006 ◽  
pp. 499-505 ◽  
Author(s):  
Lei Zou ◽  
Yansheng Lu ◽  
Huaming Zhang ◽  
Rong Hu
Keyword(s):  
Pattern Growth

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.

scholarly journals A Graph Mining Approach for Ranking and Discovering the Interesting Frequent Subgraph Patterns

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Saif Ur Rehman ◽  
Kexing Liu ◽  
Tariq Ali ◽  
Asif Nawaz ◽  
Simon James Fong
Keyword(s):  
Graph Mining ◽  
A Priori ◽  
Research Work ◽  
User Involvement ◽  
Threshold Value ◽  
Research Field ◽  
Frequent Subgraph Mining ◽  
Graph Data ◽  
Frequent Subgraph ◽  
Pattern Growth

AbstractGraph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

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