This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools are introduced. Experiments with real an...

This paper addresses the strategic level of Hybrid Make-To-Stock (MTS)/Make-To-Order (MTO) production contexts using Fuzzy Analytic Network Process (FANP). The decisions which are involved in this level are family formation and order partitioning. First, a family formation procedure is developed; then, a fuzzy analytic network process is proposed to tackle partitioning decision. Since strategic...

This paper introduces a filter, named FCF (Fuzzy Clusteringbased Filter), for removing redundant features, thus making it possible to improve the efficacy and the efficiency of data mining algorithms. FCF is based on the fuzzy partitioning of features into clusters. The number of clusters is automatically estimated from data. After the clustering process, FCF selects a subset of features from t...

Partitioning a digital circuit into modules before implementing on a single chip is key to balancing between test cost and test correctness of built-in current testing (BICT). Most partitioning methods use statistic analysis to find the threshold value and then to determine the size of a module. These methods are rigid and inflexible since IDDQ testing requires the measurement of an analog quan...

A robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter ε allowed error rate in a cluster, and the parameter...

A new, more flexible definition of fuzzy Voronoi cells is proposed as a computationally efficient alternative to Bader's Quantum Theory of Atoms in Molecules (QTAIM) partitioning of the physical space for large-scale routine calculations. The new fuzzy scheme provides atomic charges, delocalization indices, and molecular energy components very close to those obtained using QTAIM. The method is ...

A mass assignment based ID3 algorithm for learning probabilistic fuzzy decision trees is introduced. Fuzzy partitions are used to discretise continuous feature universes and to reduce complexity when universes are discrete but with large cardinalities. Furthermore, the fuzzy partitioning of classification universes facilitates the use of these decision trees in function approximation problems. ...

One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system automatically from data. In this paper, an objective function-based approach to l...

Machine learning is one of the successful application areas of fuzzy set theory and fuzzy logic, which provide soft, and thus tolerant, way of partitioning attribute domains. Theoretical results have shown that there is no (fuzzy) machine learning algorithm that is the best for all tasks. Therefore, for a particular task, it is very useful to have a tool to compare different algorithms in order...

The total energy of a molecule is presented as a sum of one- and two-atomic energy components in terms of "fuzzy" atoms, i.e., such divisions of the three-dimensional physical space into atomic regions in which the regions assigned to the individual atoms have no sharp boundaries but exhibit a continuous transition from one to another. By proper definitions the energy components are on the chem...