Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The invisible computer
Data mining for customer service support
Information and Management
A relational model of data for large shared data banks
Communications of the ACM
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
On distributing the clustering process
Pattern Recognition Letters
Dupliances: physical and virtual activity encompassed
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Methodologies, tools and languages for building ontologies: where is their meeting point?
Data & Knowledge Engineering
Using information retrieval techniques for supporting data mining
Data & Knowledge Engineering
Mining product maps for new product development
Expert Systems with Applications: An International Journal
3C intelligent home appliance control system - Example with refrigerator
Expert Systems with Applications: An International Journal
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
A semantic query approach to personalized e-catalogs service system
Journal of Theoretical and Applied Electronic Commerce Research
Mining customer knowledge for direct selling and marketing
Expert Systems with Applications: An International Journal
Mining customer knowledge for exploring online group buying behavior
Expert Systems with Applications: An International Journal
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All kinds of information technologies have been converging rapidly in recent few years from simple traditional computers to diversified multimedia information appliances, creating unprecedented technologies and devices, such as personal digital assistants (PDAs), smart cell phones, portable media players (PMPs), online console games, and set top boxes, among others. These electronic functionalities converged devices with powerful contents and functions, such as the World Wide Web, videoconferencing, e-mail, internet telephony, online gaming, digital television, and net banking, are easier to use than traditional computers but not less capable of performing daily tasks. These information technology revolutions along with rapid growing of network technology not only increased the amount of internet applications and digital contents, but also led to diversified consumer behaviors, increased competition, and opportunities. On the other hand, one-to-one marketing is different from traditional marketing methods because it focuses on customer satisfaction and is customer-oriented rather than focusing on marketing mass consumers; thus a one-to-one marketer tries to find more different products and services for the same customer. Therefore, how to establish potential cross-selling and one-to-one offers through product mix analysis, enhance relationship with customers by means of personalized offers through product knowledge, and understand users' needs and making useful suggestions for new product developments and one-to-one marketing become critical issues to information appliance firms. This paper proposes association rules, clustering analysis and CART as methodologies of data-mining, which is implemented for mining product and marketing knowledge from information users. Knowledge extraction from information users is illustrated as knowledge patterns, rules, clusters, and trees in order to propose suggestions on one-to-one marketing for information appliance firms.