DocumentCode
526328
Title
Notice of Retraction
Mining FCI of incremental attribute in Iceberg concept lattice
Author
Anrong Xue ; Fuqiang Wang ; Mingcai Zhang ; Ming Li
Author_Institution
Sch. of Comput. Sci. & Telecommun. Eng., Jiangsu Univ., Zhenjiang, China
Volume
6
fYear
2010
fDate
9-11 July 2010
Firstpage
78
Lastpage
82
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Frequent closed itemsets is a smaller, representative of the frequent itemsets. Some existed algorithms for mining frequent closed itemsets based on FP-Tree improvement or incremental mining the database with objects, thus, mining frequent closed itemsets inefficient in the database of increasing attribute. This paper, based on Iceberg concept lattice model, presents a frequent closed itemsets mining algorithm FCI-AI in the Iceberg concept lattice. Adding an attribute to database, our algorithm can use the original Iceberg concept lattice for mining the frequent closed itemsets incrementally without having to mine all frequent closed itemsets from scratch. The experimental results show that proposed FCI-AI algorithm with the advantage of less repetitive tasks and efficiency.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Frequent closed itemsets is a smaller, representative of the frequent itemsets. Some existed algorithms for mining frequent closed itemsets based on FP-Tree improvement or incremental mining the database with objects, thus, mining frequent closed itemsets inefficient in the database of increasing attribute. This paper, based on Iceberg concept lattice model, presents a frequent closed itemsets mining algorithm FCI-AI in the Iceberg concept lattice. Adding an attribute to database, our algorithm can use the original Iceberg concept lattice for mining the frequent closed itemsets incrementally without having to mine all frequent closed itemsets from scratch. The experimental results show that proposed FCI-AI algorithm with the advantage of less repetitive tasks and efficiency.
Keywords
data mining; Iceberg concept lattice model; frequent closed itemset mining algorithm; incremental attribute; incremental mining; Communications technology; Context; Databases; Frequent Closet Itemsets; Iceberg Concept Lattice; Sub-Context; Sub-Lattice;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563596
Filename
5563596
Link To Document