Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Pattern languages of program design
Pattern languages of program design
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th International Conference on Data Engineering
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Fuzzy Partitioning of Quantitative Attribute Domains by a Cluster Goodness Index
Fuzzy Partitioning of Quantitative Attribute Domains by a Cluster Goodness Index
A new distributed data mining model based on similarity
Proceedings of the 2003 ACM symposium on Applied computing
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Spatial associative classification at different levels of granularity: a probabilistic approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A unified and flexible framework for comparing simple and complex patterns
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
A Framework for High-Accuracy Privacy-Preserving Mining
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ACM SIGMOD Record
Distributed higher order association rule mining using information extracted from textual data
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ACM Computing Surveys (CSUR)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Distributed higher-order text mining: theory and practice
dg.o '06 Proceedings of the 2006 international conference on Digital government research
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
SemGrAM: integrating semantic graphs into association rule mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
Integrating pattern mining in relational databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
NaviMoz: mining navigational patterns in portal catalogs
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
An XML-based database for knowledge discovery
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Bio-surveillance Event Models, Open Source Intelligence, and the Semantic Web
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Sustainable operation and management of data center chillers using temporal data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering itemset interactions
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
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The value of knowledge obtainable by analysing large quantities of data is widely acknowledged. However, so-called primary or raw data may not always be available for knowledge discovery for several reasons. First, cooperating institutions that are interested in sharing knowledge may not be willing (or allowed) to disclose their primary data. Second, data in the form of streams are only temporarily available for processing. If stored at all, stream data are maintained in the form of synopses or derived, abstract representations of the original data. Finally, even for non-stream data, there are limits on the computation speed to be achieved -- such limits are set by hardware and firmware technologies. This problem can only be partially solved through parallelization and increased processing power. Ultimately, in many cases data must be summarized to be processed efficiently. In the light of these observations, we anticipate the need for defining and practising data mining without the luxury of primary data. To that end, we formally introduce the paradigm of Higher Order Mining as a form of data mining that is applied over non-primary, derived data or patterns. Although Higher Order Mining is a new paradigm, there are already research advances on knowledge discovery methods from patterns rather than data. We discuss them and organize them under the light of the new paradigm. We show that the HOM paradigm reveals further potential for knowledge discovery, including the delivery of rules and patterns with semantics that are closer to human intuition and are thus more appropriate for human inspection.