An updated & more efficient algorithm for mining sequential patterns
Krishnakant solanky
, Abhishek raghuvanshi
Mining sequential pattern
Tremendous amount of data being collected is increasing speedily by computerized applications around the world. Hidden in the vast data, the valuable information is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from within. Among various data mining objectives, the mining of frequent patterns has been the focus of knowledge discovery in databases. This thesis aims to investigate efficient algorithm for mining including association rules and sequential patterns. Mining sequential patterns with time constraints, such as time gaps and sliding time-window, may reinforce the accuracy of mining results. However, the capabilities to mine the time-constrained patterns were previously available only within Apriori framework. Recent studies indicate that pattern-growth methodology could speed up sequence mining.
"An updated & more efficient algorithm for mining sequential patterns", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 7, page no.67 - 69, July-2016, Available :https://ijsdr.org/papers/IJSDR1607014.pdf
Volume 1
Issue 7,
July-2016
Pages : 67 - 69
Paper Reg. ID: IJSDR_160596
Published Paper Id: IJSDR1607014
Downloads: 000347061
Research Area: Engineering
Country: ujjain, Madhaya pardesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
