Decision tree induction is one of common approaches for extracting knowledge from a sets of feature-based examples. In real world, many data occurred in a fuzzy and uncertain form. The decision tree must able to deal with such fuzzy data. This paper presents a tree construction procedure to build a fuzzy decision tree from a collection of fuzzy data by integrating fuzzy set theory and entropy. ...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy cmeans clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optima...

adomian decomposition method has been applied to solve many functional equations so far. in this article, we have used this method to solve the fuzzy differential equation under generalized differentiability. we interpret a fuzzy differential equation by using the strongly generalized differentiability. also one concrete application for ordinary fuzzy differential equation with fuzzy input data...