This article evaluates a web page-oriented representation model for document clustering, using self-organizing maps. The representation is based on heuristic combinations of criteria by means of a fuzzy rules system. The experiments show an improvement in the proposed model behaviour versus traditional representations as TF, Bin-IDF and TF-IDF, with different vector dimensions, and using a refe...

OBJECTIVE The aim of this study was to assess the prevalence of metabolic syndrome (MS) and its association with coronary artery disease (CAD) among an elderly urban population in south India. METHODS Individuals aged ≥60 years (n=222) were recruited from Phase 3 of the Chennai Urban Rural Epidemiology Study. Anthropometric and blood pressure measurements, oral glucose tolerance test, lipids ...

OBJECTIVES To compare the prevalence, agreement and phenotypic characteristics in three ethnic groups of the new International Diabetes Federation (IDF) definition of metabolic syndrome (MS) to the World Health Organization (WHO) and national cholesterol education program (NCEP) definitions. SETTING Newcastle upon Tyne, England. DESIGN Cross-sectional surveys. PARTICIPANTS Chinese (171 me...

In this paper we present experiments for global polarity classification task of Spanish tweets for TASS 2015 challenge. In our methodology, tweets representation is focused on linguistic and polarity features such as lemmatized words, filter of content words, rules of negation, among others. In addition, different transformations are used (LDA, LSI, and TF-IDF) and combined with a SVM classifie...

Abstract Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such estimates are impaired by large estimation uncertainty, shadowing their efficacy planning efforts. Here, assuming that each climate model is one representation the underlying data generating process, i.e., Ea...

In this paper, we examine the results of applying Term Frequency Inverse Document Frequency (TF-IDF) to determine what words in a corpus of documents might be more favorable to use in a query. As the term implies, TF-IDF calculates values for each word in a document through an inverse proportion of the frequency of the word in a particular document to the percentage of documents the word appear...