A two-stage heuristic approach for the newspaper delivery problem
Computers and Industrial Engineering - Special issue: IE in Korea
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Time Series Analysis: Forecasting and Control
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Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
A cluster validation index for GK cluster analysis based on relative degree of sharing
Information Sciences—Informatics and Computer Science: An International Journal
Learning algorithms for a class of neurofuzzy network and application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Mining typical patterns from databases
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base
Expert Systems with Applications: An International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Optimal inventory policy for the fuzzy newsboy problem with quantity discounts
Information Sciences: an International Journal
Water leakage forecasting: the application of a modified fuzzy evolving algorithm
Applied Soft Computing
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A problem that most newspaper companies encounter daily is how to predict the right number of newspapers to print and distribute among distinct selling points. The aim is to predict newspaper demand as accurately as possible to meet customer need and decrease loss, the number of newspaper offered but not sold. The right amount depends of the newspaper demand at different selling points and is a function of the geographical location and customer profile. Currently, demand prediction is based on values experienced in the past and on management knowledge. This paper suggests the use of predictive data mining techniques as a systematic approach to explore newspaper company database and improve predictions. Predictions require accurate forecast of the daily newspaper amount needed at each selling point. The focus of the paper is on a prediction method that uses fuzzy clustering for data base exploration and fuzzy rules together with performance scores of selling points for prediction. Experimental results using actual data show that the method is effective when compared with the current methodology, neural network-based predictors, and autoregressive forecasters. In particular, the predictive data mining technique improves on average 10% in comparison with the use of the existing approaches.