[1] In this paper, classification of a large hydrochemical data set (more than 600 water samples and 11 hydrochemical variables) from southeastern California by fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques is performed and its application to hydrochemical facies delineation is discussed. Results from both FCM and HCA clustering produced cluster centers (prot...

An approach for the detection of structural patterns in UML class diagrams is presented. It picks up some principles of the reverse engineering component of Fujaba, such as a hierarchical pattern definition and an alternating bottomup/top-down analysis. Furthermore it uses a fuzzy-like evaluation mechanism so that it is able to recognize not only entire patterns but also incomplete instances. T...

The human body is most likely to ingest microbes or disinfection by-products (DBPs) in drinking water. More than 80% of water treatment plants use chlorine as a disinfectant. Approximately 14–16% of the bladder cancers in Ontario, Canada, are attributable to the drinking waters containing relatively high levels of chlorinated by-products (CBPs). In recent studies, in addition to the chronic can...

Implementation of a neuro-fuzzy segmentation process of the MRI data is presented in this study to detect various tissues like white matter, gray matter, csf and tumor. The advantage of hierarchical self organizing map and fuzzy c means algorithms are used to classify the image layer by layer. The lowest level weight vector is achieved by the abstraction level. We have also achieved a higher va...

Hierarchical Clustering is a procedure of cluster analysis which aims to construct a hierarchy of clusters. There are two kinds of hierarchical clustering i.e. Agglomerative, which is a bottom – up approach, where all the observations start in its own cluster, and pairs of clusters are merged moving up the hierarchy, and the other one is divisive, which is a top down approach, where each observ...

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interv...