Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

Two extensions for the ALWABP: Parallel stations and collaborative approach

In this article, we introduce two new variants of the Assembly Line Worker Assignment and Balancing Problem (ALWABP) that allow parallelization of and collaboration between heterogeneous workers. These new approaches suppose an additional level of complexity in the Line Design and Assignment process, but also higher flexibility; which may be particularly useful in practical situations where the aim is to progressively integrate slow or limited workers in conventional assembly lines. We present linear models and heuristic procedures for these two new problems. Computational results show the efficiency of the proposed approaches and the efficacy of the studied layouts in different situations. (C) 2012 Elsevier B.V. All rights reserved.

International collaborative project to compare and monitor the nutritional composition of processed foods

Background: Chronic diseases are the leading cause of premature death and disability in the world with overnutrition a primary cause of diet-related ill health. Excess energy intake, saturated fat, sugar, and salt derived from processed foods are a major cause of disease burden. Our objective is to compare the nutritional composition of processed foods between countries, between food companies, and over time. Design: Surveys of processed foods will be done in each participating country using a standardized methodology. Information on the nutrient composition for each product will be sought either through direct chemical analysis, from the product label, or from the manufacturer. Foods will be categorized into 14 groups and 45 categories for the primary analyses which will compare mean levels of nutrients at baseline and over time. Initial commitments to collaboration have been obtained from 21 countries. Conclusions: This collaborative approach to the collation and sharing of data will enable objective and transparent tracking of processed food composition around the world. The information collected will support government and food industry efforts to improve the nutrient composition of processed foods around the world.