No abstract

The fuzzy clustering algorithm is applied in order to obtain the cluster structure of the chemical elements, based on their physical, chemical, and structural properties. The results obtained with the fuzzy method are consistent with the chemical behavior of the elements and with the predictions based on their electronic structure. An IBM-PC computer has been used to run the corresponding program written in Pascal. Moreover, the results suggest some new untrivial relationships between chemical elements according to the gradual nature of their properties.

In this paper we analyze the set of Greek muds from eight different locations given in ref 16 using a divisive fuzzy hierarchical cross-classification algorithm. We consider the fuzzy clustering algorithms are capable to eliminate the disfunctionalities of the hard clustering algorithms as well as to provide information obtained from a metrical analysis of the data. The fuzzy hierarchical cross-classification algorithm presented here produces not only a fuzzy partition of the muds in discussion but also a fuzzy partition of the 23 chemical and mineralogical characteristics, so that to each class of muds we may associate the class of characteristics that contributed to the separation of the class of muds.

Fuzzy logic and neural network techniques are used to classify intramolecular interactions between transition metals (M) and beta-X substituents in the following structural motif (LnMC(alpha)(A1)(A2)-C(beta)(B1)(B2)X). These interactions are relevant to the direct polymerization of functionalized olefins by Ziegler-Natta (ZN) catalysis. The efficiency and effectiveness of different soft computing techniques are compared. These methods give not only encouraging results with respect to general data mining issues but also insight into the factors that effect interactions between transition metals and beta-X substituents.