Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction

Authors:
Dan Li;Jitender S. Deogun
Affiliations:
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE;Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE
Venue:
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Year:
2005

Citing 7
Cited 5

Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Cyclic Association Rules

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Discovering Representative Episodal Association Rules from Event Sequences Using Frequent Closed Episode Sets and Event Constraints

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Efficient Mining of Partial Periodic Patterns in Time Series Database

ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovering Calendar-Based Temporal Association Rules

TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Fast vertical mining using diffsets

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

An effective mining approach for up-to-date patterns

Expert Systems with Applications: An International Journal
Implementing an efficient causal learning mechanism in a cognitive tutoring agent

IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A cognitive tutoring agent with episodic and causal learning capabilities

AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A computational model for causal learning in cognitive agents

Knowledge-Based Systems
Temporal data mining with up-to-date pattern trees

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A periodic pattern indicates something persistent and predictable, so it is important to identify and characterize the periodicity. This paper presents an approach for mining partial periodic association rules in temporal databases. This approach allows the discovery of periodic episodes such that the events in an episode are not limited to a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful in many applications for prediction. The approach is implemented using two algorithms based on two data structures, event-based linked list and window-based linked list.