Mining association rules for label ranking

Authors:
Cláudio Rebelo De Sá;Carlos Soares;Alípio Mário Jorge;Paulo Azevedo;Joaquim Costa
Affiliations:
LIAAD-INESC Porto L.A., Porto, Portugal;LIAAD-INESC Porto L.A., Porto, Portugal and Faculdade de Economia, Universidade do Porto;LIAAD-INESC Porto L.A., Porto, Portugal and DCC, Faculdade de Ciencias, Universidade do Porto;CCTC, Departamento de Informática, Universidade do Minho;DM, Faculdade de Ciencias, Universidade do Porto
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

Citing 12
Cited 3

Efficient parallel data mining for association rules

CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Constraint-Based Rule Mining in Large, Dense Databases

Data Mining and Knowledge Discovery
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results

Machine Learning
Ranking with Predictive Clustering Trees

ECML '02 Proceedings of the 13th European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint Classification: A New Approach to Multiclass Classification

ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Label ranking by learning pairwise preferences

Artificial Intelligence
Decision tree and instance-based learning for label ranking

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Ensembles of jittered association rule classifiers

Data Mining and Knowledge Discovery
A similarity-based adaptation of naive bayes for label ranking: application to the metalearning problem of algorithm recommendation

DS'10 Proceedings of the 13th international conference on Discovery science

Mining preferences from OLAP query logs for proactive personalization

ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Multilayer perceptron for label ranking

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Mining high coherent association rules with consideration of support measure

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.