Paper Info
Title
Max-FTP: mining maximal fault-tolerant frequent patterns from databases
Abstract
Mining Fault-Tolerant (FT) Frequent Patterns in real world (dirty) databases is considered to be a fruitful direction for future data mining research. In last couple of years a number of different algorithms have been proposed on the basis of Apriori-FT frequent pattern mining concept. The main limitation of these existing FT frequent pattern mining algorithms is that, they try to find all FT frequent patterns without considering only useful long (maximal) patterns. This not only increases the processing time of mining process but also generates too many redundant short FT frequent patterns that are un-useful. In this paper we present a novel concept of mining only maximal (long) useful FT frequent patterns. For mining such patterns algorithm we introduce a novel depth first search algorithm Max-FTP (Maximal Fault-Tolerant Frequent Pattern Mining), with its various search space pruning and fast frequency counting techniques. Our different extensive experimental result on benchmark datasets show that Max-FTP is very efficient in filtering un-interesting FT patterns and execution as compared to Apriori-FT.
Year
DOI
Venue
2007
10.1007/978-3-540-73390-4_26
BNCOD
Keywords
Field
DocType
apriori-ft frequent pattern mining,mining process,ft frequent pattern,future data mining research,useful ft,maximal fault-tolerant frequent pattern,frequent pattern,redundant short ft,frequent pattern mining algorithm,un-interesting ft pattern,existing ft,depth first search,fault tolerant,association rule,search space,data mining
Data mining,File Transfer Protocol,Pattern recognition,Computer science,Depth-first search,Filter (signal processing),Fault tolerance,Association rule learning,Artificial intelligence,Database
Conference
Volume
ISSN
Citations 
4587
0302-9743
2
PageRank 
References 
Authors
0.42
8
2
Name
Order
Citations
PageRank
Shariq Bashir116713.48
Abdul Rauf Baig212615.82