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 efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed 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
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Scalable data mining for rules
Scalable data mining for rules
Towards long pattern generation in dense databases
ACM SIGKDD Explorations Newsletter
Bottom-Up Association Rule Mining in Relational Databases
Journal of Intelligent Information Systems - Special issue on data warehousing and knowledge discovery
TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
On the Efficiency of Association-Rule Mining Algorithms
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Answering the Most Correlated N Association Rules Efficiently
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Direct Interesting Rule Generation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Informative Rule Set for Prediction
Journal of Intelligent Information Systems
Go Green: Recycle and Reuse Frequent Patterns
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 2005 ACM symposium on Applied computing
Finding frequent itemsets by transaction mapping
Proceedings of the 2005 ACM symposium on Applied computing
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
ACM Computing Surveys (CSUR)
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Association rules mining using heavy itemsets
Data & Knowledge Engineering
Parameter optimized, vertical, nearest-neighbor-vote and boundary-based classification
ACM SIGKDD Explorations Newsletter
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Discovering frequent geometric subgraphs
Information Systems
Enhancing quality of knowledge synthesized from multi-database mining
Pattern Recognition Letters
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Computing frequent itemsets inside oracle 10G
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Toward supporting real-time mining for data residing on enterprise systems
Expert Systems with Applications: An International Journal
CARIBIAM: Constrained Association Rules using Interactive Biological IncrementAl Mining
International Journal of Bioinformatics Research and Applications
A time- and memory-efficient frequent itemset discovering algorithm for association rule mining
International Journal of Computer Applications in Technology
Mining high utility itemsets in large high dimensional data
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
A Contribution to the Use of Decision Diagrams for Loading and Mining Transaction Databases
Fundamenta Informaticae - Special issue ISMIS'05
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Identifying appropriate methodologies and strategies for vertical mining with incomplete data
WSEAS Transactions on Computers
Issues in pattern mining and their resolutions
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Vertical mining with incomplete data
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
An efficient algorithm for mining frequent maximal and closed itemsets
International Journal of Hybrid Intelligent Systems
Performance evaluation and analysis of K-way join variants for association rule mining
BNCOD'03 Proceedings of the 20th British national conference on Databases
Mining frequent itemsets in large data warehouses: a novel approach proposed for sparse data sets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Algorithms for mining frequent itemsets in static and dynamic datasets
Intelligent Data Analysis
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
A new approach for generating efficient sample from market basket data
Expert Systems with Applications: An International Journal
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
Boolean algebra and compression technique for association rule mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Itemset mining: A constraint programming perspective
Artificial Intelligence
An improved association rules mining method
Expert Systems with Applications: An International Journal
An efficient approach for interactive mining of frequent itemsets
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Estimation of the density of datasets with decision diagrams
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
An efficient compression technique for frequent itemset generation in association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Profile association rule mining using tests of hypotheses without support threshold
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
An improvement for dEclat algorithm
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Efficient mining top-k regular-frequent itemset using compressed tidsets
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Distributed methodology of cantree construction
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Mop: An Efficient Algorithm for Mining Frequent Pattern with Subtree Traversing
Fundamenta Informaticae
A Contribution to the Use of Decision Diagrams for Loading and Mining Transaction Databases
Fundamenta Informaticae - Special issue ISMIS'05
Hierarchical clustering of XML documents focused on structural components
Data & Knowledge Engineering
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
Hi-index | 0.01 |
In a vertical representation of a market-basket database, each item is associated with a column of values representing the transactions in which it is present. The association-rule mining algorithms that have been recently proposed for this representation show performance improvements over their classical horizontal counterparts, but are either efficient only for certain database sizes, or assume particular characteristics of the database contents, or are applicable only to specific kinds of database schemas. We present here a new vertical mining algorithm called VIPER, which is general-purpose, making no special requirements of the underlying database. VIPER stores data in compressed bit-vectors called “snakes” and integrates a number of novel optimizations for efficient snake generation, intersection, counting and storage. We analyze the performance of VIPER for a range of synthetic database workloads. Our experimental results indicate significant performance gains, especially for large databases, over previously proposed vertical and horizontal mining algorithms. In fact, there are even workload regions where VIPER outperforms an optimal, but practically infeasible, horizontal mining algorithm.