Title
SmartMiner: a depth first algorithm guided by tail information for mining maximal frequent itemsets
Abstract
Maximal frequent itemsets (MR) are crucial to many tasks in data mining. Since the MaxMiner algorithm first introduced enumeration trees for mining MR in 1998, several methods have been proposed to use depth first search to improve performance. To further improve the performance of mining MR, we proposed a technique that takes advantage of the information gathered from previous steps to discover new MR. More specifically, our algorithm called SmartMiner gathers and passes tail information and uses a heuristic select function which uses the tail information to select the next node to explore. Compared with Mafia and GenMax, SmartMiner generates a smaller search tree, requires a smaller number of support counting, and does not require superset checking. Using the datasets Mushroom and Connect, our experimental study reveals that SmartMiner generates the same MFI as Mafia and GenMax, but yields an order of magnitude improvement in speed.
Year
DOI
Venue
2002
10.1109/ICDM.2002.1184003
ICDM
Keywords
Field
DocType
search space pruning.,tail information,maximal frequent pattern,frequent patterns,mushroom,tree searching,discovering association rules,smartminer,maximal frequent itemsets,connect,new mfi,datasets mushroom andconnect,heuristic select function,datasets,enumeration trees,depth firstsearch,computation complexity,maxminer algorithm,enumeration tree,mining mfi,data mining,mining maximal frequent itemsets,depth first algorithm,experimental study,transactions,computer science,testing,sampling methods,association rules,pattern analysis,tail,depth first search
Data mining,Subset and superset,Heuristic,Computer science,Enumeration,Breadth-first search,Depth-first search,Algorithm,Artificial intelligence,Machine learning,Search tree
Conference
ISBN
Citations 
PageRank 
0-7695-1754-4
23
1.34
References 
Authors
16
3
Name
Order
Citations
PageRank
Qinghua Zou113311.09
Wesley W. Chu22311789.42
Baojing Lu3291.82