Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Multi-Dimensional Modal Logic as a Framework for Spatio-Temporal Reasoning
Applied Intelligence
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Knowledge Discovery in Spatial Data by Means of ILP
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Detecting Temporal Change in Event Sequences: An Application to Demographic Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Task Modelling in Multiple Contexts of Use
DSV-IS '02 Proceedings of the 9th International Workshop on Interactive Systems. Design, Specification, and Verification
Learning Programs in the Event Calculus
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Discovering Associations between Spatial Objects: An ILP Application
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
From Shell Logs to Shell Scripts
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Context Awareness by Analyzing Accelerometer Data
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Actions and Events in Interval Temporal Logic
Actions and Events in Interval Temporal Logic
SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Learning and inferring transportation routines
Artificial Intelligence
First-order temporal pattern mining with regular expression constraints
Data & Knowledge Engineering
Journal of Artificial Intelligence Research
Mining frequent logical sequences with SPIRIT-LoG
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Mining first-order temporal interval patterns with regular expression constraints
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. In a multi-dimensional sequence each event depends on more than one dimension, such as in spatio-temporal sequences where an event may be spatially or temporally related to other events. In literature, the multi-relational data mining approach has been successfully applied to knowledge discovery fromcomplex data. However, there exists no contribution to manage the general case of multi-dimensional data in which, for example, spatial and temporal information may co-exist. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multidimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Inductive Logic Programming algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences proving its effectiveness.