Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Fast discovery of association rules
Advances in knowledge discovery and data mining
Zero knowledge and the chromatic number
Journal of Computer and System Sciences - Eleventh annual conference on structure and complexity 1996
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
SIAM Review
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Mining association rules using inverted hashing and pruning
Information Processing Letters
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
A Tight Upper Bound on the Number of Candidate Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequent Closures as a Concise Representation for Binary Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Performance Guarantees for Hierarchical Clustering
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation schemes for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering Frequent Geometric Subgraphs
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
| Hi-index | 0.00 |
In this paper we introduce a generalization of associationrules: change profiles. We analyze their properties, describetheir relationship to other structures in pattern discoveryand sketch their possible applications. We studyhow the frequent patterns can be clustered based on theirchange profiles and propose methods for approximating thefrequencies of the patterns from the approximate changeprofiles and bounding the intervals where the frequencies ofthe patterns are guaranteed to be. We evaluate empiricallythe methods for estimating the frequencies and the stabilityof their frequency estimates under different kinds of noise.