Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
A vector space model for automatic indexing
Communications of the ACM
Induction of semantic classes from natural language text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The TREC-5 Confusion Track: Comparing Retrieval Methods for Scanned Text
Information Retrieval
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Compatible Top-K Theme Patterns from Text Based on Users' Preferences
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Mining problem-solving strategies from HCI data
ACM Transactions on Computer-Human Interaction (TOCHI)
Semantic search results clustering
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
A novel evolutionary method to search interesting association rules by keywords
Expert Systems with Applications: An International Journal
Searching interesting association rules based on evolutionary computation
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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As a fundamental data mining task, frequent pattern mining has widespread applications in many different domains. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important nextstep - interpreting the discovered frequent patterns. Although some recent work has studied the compression and summarization of frequent patterns, the proposed techniques can only annotate a frequent pattern with non-semantical information (e.g. support), which provides only limited help for a user to understand the patterns.In this paper, we propose the novel problem of generating semantic annotations for frequent patterns. The goal is to annotate a frequent pattern with in-depth, concise, and structured information that can better indicate the hidden meanings of the pattern. We propose a general approach to generate such anannotation for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns. This general approach has potentially many applications such as generating a dictionary-like description for a pattern, finding synonym patterns, discovering semantic relations, and summarizing semantic classes of a set of frequent patterns. Experiments on different datasets show that our approach is effective in generating semantic pattern annotations.