FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206197977> ?p ?o ?g. }

• W4206197977 endingPage "9884" @default.
• W4206197977 startingPage "9861" @default.
• W4206197977 abstract "Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences." @default.
• W4206197977 created "2022-01-25" @default.
• W4206197977 creator A5001144935 @default.
• W4206197977 creator A5007322337 @default.
• W4206197977 creator A5032245448 @default.
• W4206197977 creator A5034766193 @default.
• W4206197977 creator A5073041905 @default.
• W4206197977 creator A5080738591 @default.
• W4206197977 date "2022-01-10" @default.
• W4206197977 modified "2023-10-06" @default.
• W4206197977 title "NetNMSP: Nonoverlapping maximal sequential pattern mining" @default.
• W4206197977 cites W1983595396 @default.
• W4206197977 cites W1988757176 @default.
• W4206197977 cites W1998959019 @default.
• W4206197977 cites W2017494415 @default.
• W4206197977 cites W2022798908 @default.
• W4206197977 cites W2028187934 @default.
• W4206197977 cites W2029536746 @default.
• W4206197977 cites W2029955663 @default.
• W4206197977 cites W2071323385 @default.
• W4206197977 cites W2073963731 @default.
• W4206197977 cites W2096126105 @default.
• W4206197977 cites W2110723042 @default.
• W4206197977 cites W2166867592 @default.
• W4206197977 cites W2278789863 @default.
• W4206197977 cites W2397052757 @default.
• W4206197977 cites W2407924934 @default.
• W4206197977 cites W2515543847 @default.
• W4206197977 cites W2537091826 @default.
• W4206197977 cites W2550062805 @default.
• W4206197977 cites W2564666952 @default.
• W4206197977 cites W2567142519 @default.
• W4206197977 cites W2588254752 @default.
• W4206197977 cites W2588614855 @default.
• W4206197977 cites W2758905143 @default.
• W4206197977 cites W2758942695 @default.
• W4206197977 cites W2762034449 @default.
• W4206197977 cites W2774676107 @default.
• W4206197977 cites W2777804614 @default.
• W4206197977 cites W2784172695 @default.
• W4206197977 cites W2789717644 @default.
• W4206197977 cites W2792358221 @default.
• W4206197977 cites W2799813871 @default.
• W4206197977 cites W2802410939 @default.
• W4206197977 cites W2810412011 @default.
• W4206197977 cites W2883724305 @default.
• W4206197977 cites W2894761743 @default.
• W4206197977 cites W2904161907 @default.
• W4206197977 cites W2905695809 @default.
• W4206197977 cites W2907464021 @default.
• W4206197977 cites W2909794169 @default.
• W4206197977 cites W2937863287 @default.
• W4206197977 cites W2949915660 @default.
• W4206197977 cites W2973857687 @default.
• W4206197977 cites W2973918817 @default.
• W4206197977 cites W2976436416 @default.
• W4206197977 cites W2984681103 @default.
• W4206197977 cites W2989566844 @default.
• W4206197977 cites W3003217347 @default.
• W4206197977 cites W3006291958 @default.
• W4206197977 cites W3013769259 @default.
• W4206197977 cites W3015232492 @default.
• W4206197977 cites W3021053579 @default.
• W4206197977 cites W3038220444 @default.
• W4206197977 cites W3041954559 @default.
• W4206197977 cites W3042648958 @default.
• W4206197977 cites W3046961612 @default.
• W4206197977 cites W3088781576 @default.
• W4206197977 cites W3097720646 @default.
• W4206197977 cites W3102927519 @default.
• W4206197977 cites W3120721672 @default.
• W4206197977 cites W3130153679 @default.
• W4206197977 cites W3162911445 @default.
• W4206197977 cites W3169551350 @default.
• W4206197977 cites W3177259441 @default.
• W4206197977 cites W3187369430 @default.
• W4206197977 cites W3191240255 @default.
• W4206197977 cites W3199713267 @default.
• W4206197977 cites W3210621838 @default.
• W4206197977 cites W4210775951 @default.
• W4206197977 cites W863926701 @default.
• W4206197977 doi "https://doi.org/10.1007/s10489-021-02912-3" @default.
• W4206197977 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35035093" @default.
• W4206197977 hasPublicationYear "2022" @default.
• W4206197977 type Work @default.
• W4206197977 citedByCount "10" @default.
• W4206197977 countsByYear W42061979772022 @default.
• W4206197977 countsByYear W42061979772023 @default.
• W4206197977 crossrefType "journal-article" @default.
• W4206197977 hasAuthorship W4206197977A5001144935 @default.
• W4206197977 hasAuthorship W4206197977A5007322337 @default.
• W4206197977 hasAuthorship W4206197977A5032245448 @default.
• W4206197977 hasAuthorship W4206197977A5034766193 @default.
• W4206197977 hasAuthorship W4206197977A5073041905 @default.
• W4206197977 hasAuthorship W4206197977A5080738591 @default.
• W4206197977 hasBestOaLocation W42061979771 @default.
• W4206197977 hasConcept C11413529 @default.
• W4206197977 hasConcept C124101348 @default.

• next