Scalable, Distributed and Dynamic Mining of Association Rules

Authors:
V. S. Ananthanarayana;D. K. Subramanian;M. N. Murty
Affiliations:
-;-;-
Venue:
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Year:
2000

Citing 4
Cited 5

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Parallel and Distributed Association Mining: A Survey

IEEE Concurrency
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases

A High-Performance Distributed Algorithm for Mining Association Rules

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Finding the boundaries of attributes domains of quantitative association rules using abstraction: a dynamic approach

ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Mining single pass weighted pattern tree

ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Attribute -TID method for discovering sequence of attributes

ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Toward the scalability of neural networks through feature selection

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel pattern tree called Pattern Count tree (PC-tree) which is a complete and compact representation of the database. We show that construction of this tree and then generation of all large itemsets requires a single database scan where as the current algorithms need at least two database scans. The completeness property of the PCtree with respect to the database makes it amenable for mining association rules in the context of changing data and knowledge, which we call dynamic mining. Algorithms based on PC-tree are scalable because PC-tree is compact. We propose a partitioned distributed architecture and an efficient distributed association rule mining algorithm based on the PC-tree structure.