C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating campaign benefits and modeling lift
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction with local patterns using cross-entropy
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning When Negative Examples Abound
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICML '99 Proceedings of the Sixteenth International 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
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
On the strength of hyperclique patterns for text categorization
Information Sciences: an International Journal
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Measuring similarity in feature space of knowledge entailed by two separate rule sets
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
Building a highly-compact and accurate associative classifier
Applied Intelligence
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Adjusting and generalizing CBA algorithm to handling class imbalance
Expert Systems with Applications: An International Journal
2-PS based associative text classification
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Information Sciences: an International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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In many data mining applications, the objective is to select data cases of a target class. For example, in direct marketing, marketers want to select likely buyers of a particular product for promotion. In such applications, it is often too difficult to predict who will definitely be in the target class (e.g., the buyer class) because the data used for modeling is often very noisy and has a highly imbalanced class distribution. Traditionally, classification systems are used to solve this problem. Instead of classifying each data case to a definite class (e.g., buyer or non-buyer), a classification system is modified to produce a class probability estimate (or a score) for the data case to indicate the likelihood that the data case belongs to the target class (e.g., the buyer class). However, existing classification systems only aim to find a subset of the regularities or rules that exist in data. This subset of rules only gives a partial picture of the domain. In this paper, we show that the target selection problem can be mapped to association rule mining to provide a more powerful solution to the problem. Since association rule mining aims to find all rules in data, it is thus able to give a complete picture of the underlying relationships in the domain. The complete set of rules enables us to assign a more accurate class probability estimate to each data case. This paper proposes an effective and efficient technique to compute class probability estimates using association rules. Experiment results using public domain data and real-life application data show that in general the new technique performs markedly better than the state-of-the-art classification system C4.5, boosted C4.5, and the Naïve Bayesian system.