Search academic resources

1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monot...

The fuzzy logic controller (FLC) provides a means of converting a linguistic control strategy. A survey of the FLC is presented, and a general methodology for constructing an FLC and assessing its performance is describe...

Typologies are an important way of organizing the complex cause-effect relationships that are key building blocks of the strategy and organization literatures. Here, I develop a novel theoretical perspective on causal co...

The approach described in this paper represents a substantive departure from the conventional quantitative techniques of system analysis. It has three main distinguishing features: 1) use of so-called ``linguistic'' vari...

With the shift towards a more sustainable energy system, the need for a better understanding of the behavior development over time of consumers and prosumers arises. Despite the growing penetration of smart meter infrast...

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a l...

Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied t...

Previous article Next article Fractional Brownian Motions, Fractional Noises and ApplicationsBenoit B. Mandelbrot and John W. Van NessBenoit B. Mandelbrot and John W. Van Nesshttps://doi.org/10.1137/1010093PDFBibTexSecti...

In this Ph. D thesis, the main significant contributions are: formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in...

In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared dist...

Subjects / keywords: Cluster analysis; Weighting; Fortran; Algorithm; Fuzzy logic; Computer science; Diagonal; Fuzzy clustering; Data mining; Mathematics; Artificial intelligence; Operating system

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disci...

Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data ...

Subjects / keywords: Cluster analysis; Computer science; Correlation clustering; Consensus clustering; CURE data clustering algorithm; Fuzzy clustering; Single-linkage clustering; Conceptual clustering; Artificial intelligence; Clustering hi...

E-mail texts are hard to process due to their short length. In this article, the use of a diffuse neural network that is capable of identifying the most relevant terms in a set of e-mails is proposed. The associations be...