TCOM, an innovative data structure for mining association rules among infrequent items

Authors:
Junfeng Ding;Stephen S. T. Yau
Affiliations:
Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, 851 South Morgan Chicago, IL 60607, United States;Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, 851 South Morgan Chicago, IL 60607, United States
Venue:
Computers & Mathematics with Applications
Year:
2009

Citing 15
Cited 2

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient search for association rules

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets

Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meterological data

Data & Knowledge Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Optimized Association Rules with Categorical and Numeric Attributes

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
Constraint-Based Rule Mining in Large, Dense Databases

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Association-rules-based recommender system for personalization in adaptive web-based applications

ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
An improved association rules mining method

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.09

Visualization

Abstract

Association rule mining is one of the most important areas in data mining, which has received a great deal of attention. The purpose of association rule mining is the discovery of association relationships or correlations among a set of items. In this paper, we present an efficient way to find the valid association rules among the infrequent items, which is seldom mentioned and whose importance often get ignored by other researchers. We design a new data structure, called Transactional Co-Occurrence Matrix, in short TCOM, by two passing of the original transactional database. Then the occurrence count of the itemsets and valid association rules will be mined based on TCOM, which combines the advantages of both transactional oriented (horizontal) layout and item oriented (vertical) layout of the database. It turns out that any itemsets could be randomly accessed and counted without full scan of either the original database or the TCOM, which significantly improves the efficiency of the mining processes.