Frequent pattern mining: current status and future directions

Data mining: concepts and techniques

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Mining association rules between sets of items in large databases

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Fast algorithms for mining association rules

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Fast Algorithms for Mining Association Rules in Large Databases

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Mining frequent patterns without candidate generation

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Mining sequential patterns

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Mining frequent patterns without candidate generation

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Mining sequential patterns: Generalizations and performance improvements

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Automatic subspace clustering of high dimensional data for data mining applications

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Integrating classification and association rule mining

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Web usage mining: discovery and applications of usage patterns from Web data

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gSpan: graph-based substructure pattern mining

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Dynamic itemset counting and implication rules for market basket data

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SPADE: An Efficient Algorithm for Mining Frequent Sequences

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PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth

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Scalable algorithms for association mining

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Mining generalized association rules

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An Efficient Algorithm for Mining Association Rules in Large Databases

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Discovery of Frequent Episodes in Event Sequences

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Web mining research: a survey

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An effective hash-based algorithm for mining association rules

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Discovering Frequent Closed Itemsets for Association Rules

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Mining sequential patterns by pattern-growth: the PrefixSpan approach

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Efficiently mining long patterns from databases

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Beyond market baskets: generalizing association rules to correlations

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CMAR: accurate and efficient classification based on multiple class-association rules

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Approximate frequency counts over data streams

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Sampling Large Databases for Association Rules

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Frequent subgraph discovery

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Parallel mining of association rules

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An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

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Efficient mining of emerging patterns: discovering trends and differences

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Approximate Frequency Counts over Data Streams

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CHARM: An Efficient Algorithm for Closed Itemset Mining

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Discovery of Multiple-Level Association Rules from Large Databases

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Selecting the right interestingness measure for association patterns

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CPAR: Classification based on Predictive Association Rules

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CLOSET : An Efficient Algorithm for Mining Frequent Closed Itemsets

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Maintenance of discovered association rules in large databases: an incremental updating technique

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Exploratory mining and pruning optimizations of constrained associations rules

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Efficient Computation of Frequent and Top-k Elements in Data Streams

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Graph indexing: a frequent structure-based approach

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CloseGraph: mining closed frequent graph patterns

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Efficient algorithms for discovering association rules

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BIDE: efficient mining of frequent closed sequences

Wang, J., & Han, J. (2004) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2004.1319986 · Cited by 616

Efficient mining of frequent subgraphs in the presence of isomorphism

Huan, J., Wang, W., & Prins, J. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250974 · Cited by 614

MAFIA: a maximal frequent itemset algorithm for transactional databases

Burdick, D., Calimlim, M., & Gehrke, J. (2002) · Proceedings 17th International Conference on Data Engineering · doi:10.1109/icde.2001.914857 · Cited by 612

Efficient mining of partial periodic patterns in time series database

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CLOSET+: searching for the best strategies for mining frequent closed itemsets

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Entropy-based subspace clustering for mining numerical data

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Frequent term-based text clustering

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A Tree Projection Algorithm for Generation of Frequent Item Sets

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CloSpan: Mining: Closed Sequential Patterns in Large Datasets

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Mining Access Patterns Efficiently from Web Logs

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A simple algorithm for finding frequent elements in streams and bags

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Clustering by pattern similarity in large data sets

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Efficiently mining frequent trees in a forest

Zaki, M. J. (2002) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775058 · Cited by 472

Bottom-up computation of sparse and Iceberg CUBE

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Efficiently Using Prefix-trees in Mining Frequent Itemsets.

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Discovery of spatial association rules in geographic information databases

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State of the art of graph-based data mining

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SPIRIT: Sequential Pattern Mining with Regular Expression Constraints

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Mining molecular fragments: finding relevant substructures of molecules

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A fast distributed algorithm for mining association rules

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Alternative interest measures for mining associations in databases

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PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code

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Cyclic association rules

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Efficient Substructure Discovery from Large Semi-structured Data

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Discriminative Frequent Pattern Analysis for Effective Classification

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CP-Miner: a tool for finding copy-paste and related bugs in operating system code

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Data mining for path traversal patterns in a web environment

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Mining frequent itemsets with convertible constraints

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Scalable Techniques for Mining Causal Structures

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Substructure similarity search in graph databases

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Integrating association rule mining with relational database systems: alternatives and implications

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Parallel Algorithms for Discovery of Association Rules

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Mining All Non-derivable Frequent Itemsets

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Mining Frequent Spatio-Temporal Sequential Patterns

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Association rules over interval data

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Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization

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Bottom-up computation of sparse and Iceberg CUBE

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Advances in frequent itemset mining implementations: report on FIMI'03

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A new framework for itemset generation

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Mining partially periodic event patterns with unknown periods

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Efficient computation of Iceberg cubes with complex measures

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Multi-dimensional sequential pattern mining

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TFP: an efficient algorithm for mining top-k frequent closed itemsets

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Dwarf: shrinking the PetaCube

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Metarule-Guided Mining of Multi-Dimensional Association RulesUsing Data Cubes

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Efficient parallel data mining for association rules

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Mining compressed frequent-pattern sets

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Mining frequent item sets by opportunistic projection

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The complexity of mining maximal frequent itemsets and maximal frequent patterns

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Summarizing itemset patterns: a profile-based approach

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Item Sets That Compress

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Clustering by pattern similarity in large data sets

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Efficient mining of constrained correlated sets

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Frequent sub-structure-based approaches for classifying chemical compounds

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A statistical theory for quantitative association rules

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Optimization of constrained frequent set queries with 2-variable constraints

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WFIM: Weighted Frequent Itemset Mining with a weight range and a minimum weight

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Mining top-K covering rule groups for gene expression data

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Efficient discovery of error-tolerant frequent itemsets in high dimensions

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Mining closed relational graphs with connectivity constraints

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Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints

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HARMONY: Efficiently Mining the Best Rules for Classification

Wang, J., & Karypis, G. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.19 · Cited by 145

Efficiently mining frequent trees in a forest

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Approximating a collection of frequent sets

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An Algorithm for In-Core Frequent Itemset Mining on Streaming Data

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Star-Cubing

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Mining Behavior Graphs for “Backtrace” of Noncrashing Bugs

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Interestingness of frequent itemsets using Bayesian networks as background knowledge

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CoMine: efficient mining of correlated patterns

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A Perspective on Databases and data mining

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Cubegrades: Generalizing Association Rules

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On Closed Constrained Frequent Pattern Mining

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Mining Colossal Frequent Patterns by Core Pattern Fusion

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ExAnte: Anticipated Data Reduction in Constrained Pattern Mining

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CLUSEQ: efficient and effective sequence clustering

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Optimization of constrained frequent set queries with 2-variable constraints

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Generating semantic annotations for frequent patterns with context analysis

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Motion-Alert: Automatic Anomaly Detection in Massive Moving Objects

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DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

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Efficient Mining of Maximal Sequential Patterns Using Multiple Samples

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PLANMINE: sequence mining for plan failures

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