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A Clustering Method for Weak Signals to Support Anticipative Intelligence
Antonio Leonardo Martins Moreira, Thomas William Norio Hayashi, Guilherme Palermo Coelho, Ana Estela Antunes Silva
Pages - 1 - 14     |    Revised - 31-12-2014     |    Published - 31-1-2015
Volume - 6   Issue - 1    |    Publication Date - January 2015  Table of Contents
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KEYWORDS
Anticipative Information, Weak Signals, Clustering, Decision Making Process, Text Mining, Similarity Function.
ABSTRACT
Organizations need appropriate anticipative information to support their decision making process. Contrarily to some strategic information analyses that help managers to establish patterns using past information, anticipative intelligence is intended to help managers to act based on the analysis of pieces of information that indicate some sort of trend that may become true in the future. One example of this kind of information is known as a weak signal, which is a short text related to a specific domain. In this work, pairs of weak signals, written in Portuguese, are compared to each other so that similarities can be identified and correlated weak signals can be clustered together. The idea is that the analysis of the resulting similar groups may lead to the formulation of a hypothesis that can support the decision making process. The proposed technique consists of two main steps: preprocessing the set of weak signals and clustering. The proposed method was evaluated on a database of bio-energy weak signals. The main innovations of this work are: (i) the application of a computational methodology from the literature for analyzing anticipative information; and (ii) the adaptation of data mining techniques to implement this methodology in a software product.
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Mr. Antonio Leonardo Martins Moreira
Unicamp - Brazil
antoniolmmoreira@gmail.com
Mr. Thomas William Norio Hayashi
Unicamp - Brazil
Dr. Guilherme Palermo Coelho
Unicamp - Brazil
Dr. Ana Estela Antunes Silva
State University of Campinas - Brazil