Understanding Customers' Affective Needs with Linguistic Summarization

Abstract: To increase the chance of launching a successful product into market, it is essential to satisfy customers’ affective needs during the product design stage. However, understanding customers’ affective needs is very difficult task and product designers might misunderstand the customers’ affective needs. In this study, linguistic summarization with fuzzy set is used to present customers’ affective needs with natural language statements which could be easily understood by human beings. The relations between customers’ affective needs and product design elements are represented by type-I and type-II fuzzy quantified sentences. To illustrate the applicability of the linguistic summarization with fuzzy set in translating customers’ affective needs to natural language statements, a case study is conducted on mobile phone design. The results indicate that the linguistic summarization with fuzzy set can be a useful tool to assist designers to create products satisfying affective needs of customer

Learning fuzzy rules with their implication operators

International audienceFuzzy predicates have been incorporated into machine learning and data mining to extend the types of data relationships that can be represented, to facilitate the interpretation of rules in linguistic terms, and to avoid unnatural boundaries in partitioning attribute domains. The confidence of an association is classically measured by the co-occurrence of attributes in tuples in the database. The semantics of fuzzy rules, however, is not co-occurrence but rather graduality or certainty and is determined by the implication operator that defines the rule. In this paper we present a learning algorithm, based on inductive logic programming, that simultaneously learns the semantics and evaluates the validity of fuzzy rules. The learning algorithm selects the implication that maximizes rule confidence while trying to be as informative as possible. The use of inductive logic programming increases the expressive power of fuzzy rules while maintaining their linguistic interpretability

Learning Fuzzy Rules with their Implication Operators

Fuzzy predicates have been incorporated into machine learning and data mining to extend the types of data relationships that can be represented, to facilitate the interpretation of rules in linguistic terms, and to avoid unnatural boundaries in partitioning attribute domains. The confidence of an association is classically measured by the co-occurrence of attributes in tuples in the database. The semantics of fuzzy rules, however, is not co-occurrence but rather graduality or certainty and is determined by the implication operator that defines the rule. In this paper we present a learning algorithm, based on inductive logic programming, that simultaneously learns the semantics and evaluates the validity of fuzzy rules. The learning algorithm selects the implication that maximizes rule confidence while trying to be as informative as possible. The use of inductive logic programming increases the expressive power of fuzzy rules while maintaining their linguistic interpretability