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This paper presents a new architecture of a fuzzy decision tree based on fuzzy rules – fuzzy rule based decision tree (FRDT) and provides a learning algorithm. In contrast with “traditional” axis-parallel decision trees in which only a single feature (variable) is taken into account at each node, the node of the proposed decision trees involves a fuzzy rule which involves multiple features. Fuzzy rules are employed to produce leaves of high purity. Using multiple features for a node helps us minimize the size of the trees. The growth of the FRDT is realized by expanding an additional node composed of a mixture of data coming from different classes, which is the only non-leaf node of each layer. This gives rise to a new geometric structure endowed with linguistic terms which are quite different from the “traditional” oblique decision trees endowed with hyperplanes as decision functions. A series of numeric studies are reported using data coming from UCI machine learning data sets. The comparison is carried out with regard to “traditional” decision trees such as C4.5, LADtree, BFTree, SimpleCart, and NBTree. The results of statistical tests have shown that the proposed FRDT exhibits the best performance in terms of both accuracy and the size of the produced trees. [ABSTRACT FROM AUTHOR]

In the framework of Axiomatic Fuzzy Set (AFS) theory, we propose a new approach to data clustering. The objective of this clustering is to adhere to some principles of grouping exercised by humans when determining a structure in data. Compared with other clustering approaches, the proposed approach offers more detailed insight into the cluster's structure and the underlying decision making process. This contributes to the enhanced interpretability of the results via the representation capabilities of AFS theory. The effectiveness of the proposed approach is demonstrated by using real-world data, and the obtained results show that the performance of the clustering is comparable with other fuzzy rule-based clustering methods, and benchmark fuzzy clustering methods FCM and K -means. Experimental studies have shown that the proposed fuzzy clustering method can discover the clusters in the data and help specify them in terms of some comprehensive fuzzy rules. [ABSTRACT FROM AUTHOR]

Traditionally, clustering is the task of dividing samples into homogeneous clusters based on their degrees of similarity. As samples are assigned to clusters, users need to manually give descriptions for all clusters. In this paper, a rapid fuzzy rule clustering method based on granular computing is proposed to give descriptions for all clusters. A new and simple unsupervised feature selection method is employed to endow every sample with a suitable description. Exemplar descriptions are selected from sample's descriptions by relative frequency, and data granulation is guided by the selected exemplar fuzzy descriptions. Every cluster is depicted by a single fuzzy rule, which make the clusters understandable for humans. The experimental results show that our proposed model is able to discover fuzzy IF–THEN rules to obtain the potential clusters. [ABSTRACT FROM AUTHOR]

Abstract: Fourier transform infrared (FTIR) method was used to study the conformations of soy protein obtained through aqueous solution and bis(2-ethylhexyl) sodium sulfosuccinate (AOT) reverse micelle extraction. The results showed that there were changes in signal intensity and/or position of IR bands at 4000–400 cm−1 when the soy proteins were separated by two extraction methods. The FTIR spectral changes were subsequently assessed using the second derivative spectroscopy in the amide I region (1700–1600 cm−1). The contents of α-helix, β-sheet, turn and irregular conformations for soy proteins using aqueous solution extraction were 11.6%, 32.8%, 44.3% and 11.3%, respectively; while using AOT reverse micelle extraction 13.1%, 41.6%, 32.5% and 12.9%, respectively. The amount change of these structures might affect functional properties of soy proteins. [Copyright &y& Elsevier]