Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Background for association rules and cost estimate of selected mining algorithms
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Online algorithms for finding profile association rules
Proceedings of the seventh international conference on Information and knowledge management
Scalable algorithms for mining large databases
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
Using Association Rules as Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web mining and its SQL based parallel execution
ITVE '01 Proceedings of the workshop on Information technology for virtual enterprises
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
An Adaptive Algorithm for Mining Association Rules on Shared-Memory Parallel Machines
Distributed and Parallel Databases
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A Graph-Based Approach for Discovering Various Types of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Effect of Data Skewness and Workload Balance in Parallel Data Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Online Generation of Profile Association Rules
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Web Log Mining and Parallel SQL Based Execution
DNIS '00 Proceedings of the International Workshop on Databases in Networked Information Systems
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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Data Access Paths for Frequent Itemsets Discovery
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Mining Generalized Association Rule Using Parallel RDB Engine on PC Cluster
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Performance Evaluation and Optimization of Join Queries for Association Rule Mining
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
SQL Based Association Rule Mining Using Commercial RDBMS (IBM DB2 UBD EEE)
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Integrating Data Mining with Relational DBMS: A Tightly-Coupled Approach
NGIT '99 Proceedings of the 4th International Workshop on Next Generation Information Technologies and Systems
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Optimal Class Association Rule Set
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Efficient Parallel Algorithms for Mining Associations
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
An interactive visualization system for mining association rules
Data mining, rough sets and granular computing
ADMiRe: an algebraic approach to system performance analysis using data mining techniques
Proceedings of the 2003 ACM symposium on Applied computing
Mining Informative Rule Set for Prediction
Journal of Intelligent Information Systems
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ADMiRe: An Algebraic Data Mining Approach to System Performance Analysis
IEEE Transactions on Knowledge and Data Engineering
A further study in the data partitioning approach for frequent itemsets mining
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
An XML-enabled data mining query language: XML-DMQL
International Journal of Business Intelligence and Data Mining
Identification of Co-regulated Signature Genes in Pancreas Cancer- A Data Mining Approach
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Identifying appropriate methodologies and strategies for vertical mining with incomplete data
WSEAS Transactions on Computers
Vertical mining with incomplete data
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
High Frequent Value Reduct in Very Large Databases
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Performance evaluation and analysis of K-way join variants for association rule mining
BNCOD'03 Proceedings of the 20th British national conference on Databases
DWMiner: a tool for mining frequent item sets efficiently in data warehouses
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Decomposing data mining by a process-oriented execution plan
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Analysis of measures of quantitative association rules
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Mining interesting XML-enabled association rules with templates
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
A relational query primitive for constraint-based pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
An artificial immune system approach to associative classification
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
ML-DS: a novel deterministic sampling algorithm for association rules mining
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
AC-CS: an immune-inspired associative classification algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
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Describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss the optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. SETM uses only simple database primitives, viz. sorting and merge-scan join. SETM is simple, fast and stable over the range of parameter values. The major contribution of this paper is that it shows that at least some aspects of data mining can be carried out by using general query languages such as SQL, rather than by developing specialized black-box algorithms. The set-oriented nature of SETM facilitates the development of extensions.