Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative Interest Measures for Mining Associations in Databases
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
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
SUMMARY: Efficiently Summarizing Transactions for Clustering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mining condensed frequent-pattern bases
Knowledge and Information Systems
Microdata protection through approximate microaggregation
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
An approximate microaggregation approach for microdata protection
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Frequent pattern mining is an important data mining problem with wide applications. The huge number of discovered frequent patterns pose great challenge for users to explore and understand them. It is desirable to accurately summarizing the set of frequent patterns into a small number of patterns or profiles so that users can easily explore them. In this paper, we employ a probability model to represent a set of frequent patterns and give two methods of estimating the support of a pattern from the model. Based on the model, we develop an approach to grouping a set of frequent patterns into k profiles and the support of frequent pattern can be estimated fairly accurately from a relative small number of profiles. Empirical studies show that our method can achieve compact and accurate summarization in real-life data and the support of frequent patterns can be restored much more accurately than the previous method.