Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.Index Terms-Data mining, demand management, electrical customer segmentation, load patterns, self-organizing maps (SOMs). . His research activities include distribution system analysis, electricity markets, demand modeling and aggregation, distributed energy resources, energy efficiency, and demand-side management and response. His research activities include electricity markets, demand modeling and aggregation, demand-side bidding, and electrical customer classification.

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Abstract--This paper shows the capacity of modern computational techniques such as the self-organizing map (SOM) as a methodology to achieve the classification of the electrical customers in a commercial or geographical area. This approach allows to extract the pattern of customer behavior from historic load demand series. Several ways of data analysis from load curves can be used to get different input data to "feed" the neural network. In this work, we propose two methods to improve customer clustering: the use of frequency-based indices and the use of the hourly load curve. Results of a case study developed on a set of different spanish customers and a comparison between the two approachs proposed here are presented.

This chapter presents the case of Mexico, which reveals the fact that district/provincial expenditures in health care are inversely related to need, a case in point of the “inverse care law.” Mexico is a middle-income country facing a double burden of infectious and chronic disease and engaged in health sector reform and decentralization. Using the marginality index developed by the Mexican government, the chapter employs a policy-relevant mechanism for highlighting disparity and mapping the intersection of marginality, membership in an indigenous group, and rural poverty. It outlines the implications of the analyses for resource allocation to meet the differential needs for health care more equitably in marginalized areas.

One of the primary obstacles in the implementation of continuous quality improvement (CQI) programmes in developing countries is the lack of timely and appropriate information for decentralized decision-making. The integrated quality information system (QIS) described herein demonstrates Mexico's unique effort to package four separate, yet mutually reinforcing, tools for the generation and use of quality-related information at all levels of the Mexican national health care system. The QIS is one element of the continuous quality improvement programme administered by the Secretariat of Health in Mexico. Mexico's QIS was designed to be flexible and capable of adapting to local needs, while at the same time allowing for the standardization of health care quality assurance indicators, and subsequent ability to measure and compare the quality performance of health facilities nationwide. The flexibility of the system extends to permit the optimal use of available data by health care managers at all levels of the health care system, as well as the generation of new information in important areas often neglected in more traditional information systems. Mexico's QIS consists of four integrated components: 1) a set of client and provider surveys, to assess specific issues in the quality of health services delivered; 2) client and provider national satisfaction surveys; 3) a sentinel health events strategy; and 4) a national Comparative Performance Evaluation System, for use by the Secretariate of Health for the quality assessment of state and provincial health care services (internal benchmarking). The QIS represents another step in Mexico's ongoing effort to use data for effective decision-making in the planning, monitoring and evaluation of services delivered by the national health care system. The design and application of Mexico's QIS provides a model for decentralized decision-making that could prove useful for developing countries, where the effective use of quality indicators is often limited. Further, the system could serve as a mechanism for motivating positive change in the way information is collected and used in the process of ensuring high quality health care service delivery.

The objective of this paper is to show the capability of the Self-Organizing Maps (SOMs) to organize, to filter, to classify and to extract patterns from distributor, commercializer, aggregator or customer electrical demand databases-objective known as data mining-. This approach basically uses-to reach the above mentioned objectives-the historic load demand curves of each user. In our case, and for simplicity, we will study two typical medium users: an industry and a university located both in Spain. The results clearly show the suitability of SOM approach to improve data management and to find easily coherent clusters between electrical users.

El presente trabajo plantea la necesidad de discutir la validez conceptual de Candelaria, una de las categorías constitutivas de la Arqueología de las selvas meridionales del Noroeste argentino. A partir de una exhaustiva revisión historiográfica, se propone una relectura crítica que contextualiza, confronta y compara las distintas construcciones narrativas desarrolladas desde la disciplina para el centro-norte de Tucumán y centro-sur de Salta a lo largo del último siglo. La evaluación de la urdimbre histórico-conceptual desde la cual se construyó Candelaria, así como nuevos datos e investigaciones realizadas en el sector, permiten plantear distintas problemáticas en relación con su utilización como una categoría arqueológica adecuada.