C4.5: programs for machine learning
C4.5: programs for machine learning
A machine discovery from amino acid sequences by decision trees over regular patterns
Selected papers of international conference on Fifth generation computer systems 92
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying prospective customers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Pharmacy Data Helps to Make Profits
Data Mining and Knowledge Discovery
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Weighted Majority Decision among Several Region Rules for Scientific Discovery
DS '99 Proceedings of the Second International Conference on Discovery Science
A Practical Algorithm to Find the Best Subsequence Patterns
DS '00 Proceedings of the Third International Conference on Discovery Science
Journal of Management Information Systems - Special section: Data mining
Journal of Management Information Systems - Special section: Data mining
Finding Best Patterns Practically
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Is this brand ephemeral? A multivariate tree-based decision analysis of new product sustainability
Decision Support Systems
Knowledge discovery from click stream data and effective site management
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
String analysis technique for shopping path in a supermarket
Journal of Intelligent Information Systems
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This paper presents a new application for discovering useful knowledge from purchase history that can be helpful to create effective marketing strategy, using a machine learning algorithm, BONSAI, proposed by Shimozono et al. in 1994 which was originally developed for analyzing string patterns developed for knowledge discovery from amino acid sequences. In order to adapt BONSAI to our purpose, we translate purchase history of customers into character strings such that each symbol represents a brand purchased by a customer. For our purpose, we extend BONSAI in the following aspects; 1) While original BONSAI generates a decision tree over regular patterns which are limited to sub-strings, we extend it to subsequences. 2) We generate rules which contain not only regular patterns but numerical attributes such as age, the number of visits, profit and etc. 3) We extend regular expression so that we can consider whether a certain pattern occurs in some latter part of the whole string. 4) We implement majority voting based on 1-D and 2-D region rules on top of decision trees.Applying the BONSAI extended in this manner to real customers' purchase history of drugstore chain in Japan, we have succeeded in generating interesting business rules which practitioners have not yet recognized.