C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Analyzing the Interestingness of Association Rules from the Temporal Dimension
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
A framework for discovering interesting business changes from data
BT Technology Journal
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
DTMC: an actionable e-customer lifetime value model based on markov chains and decision trees
Proceedings of the ninth international conference on Electronic commerce
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
Prioritization of association rules in data mining: Multiple criteria decision approach
Expert Systems with Applications: An International Journal
Detection of the customer time-variant pattern for improving recommender systems
Expert Systems with Applications: An International Journal
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
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Customers are the most important resources for department stores' revenue. Customers' needs and behavior are changed by several factors such as department stores competition and advertisements. The more inattention to the customers' behavioral changes, the more customers' defection and profit decrease, so managers should find effective solutions for detecting these changes and the amount of their importance. They need some ranking methods for correcting and on time decision making for increasing customers' long term profit and loyalty. This work is the first time that decision making criteria are selected base on long term goals of customer relationship management and fuzziness of decision making criteria is considered and changes in customers' behavioral patterns are ranked by fuzzy multi criteria decision making method. The dataset of Mondrian food mart department store have been used. This dataset includes customers' data of two years sales of Mondrian food mart department store.