Home   >   CSC-OpenAccess Library   >    Manuscript Information
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
Anticipative Information, Weak Signals, Clustering, Decision Making Process, Text Mining, Similarity Function.
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.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Ozaki; A. Del Rey and F.C. Almeida. “Radar de Monitoramento Tecnológico: Uma Ferramenta de Interpretação de Sinais Fracos para Identificação de Surpresas Estratégicas”. Future Studies Research Journal, vol. 3(1), pp. 84-110, 2011.
A.R. Coelho. “Stemming para a língua portuguesa: estudo, análise e melhoria do algoritmo RSLP”. Undergraduate Paper, Federal University of Rio Grande do Sul (UFRGS), Brazil, 2007.
C. Ting; N. Xiao and Y. Weiping. “The Application of Web Data Mining Technique in Competitive Intelligence System of Enterprise based on XML”, in Proc. of the 3rd International Symposium on Intelligent Information Technology Application, 2009, pp. 396- 399.
C.D. Manning; P. Raghavan and H. Schütze. An Introduction to Information Retrieval. Cambridge, MA: Cambridge University Press, 2008.
E. G. Maziero; T.A.S. Pardo; A. Di Felippo and B. C. Dias-da-Silva. “A base de dados lexical e a interface web do TeP 2.0: thesaurus eletrônico para o Português do Brasil”, in Proc. of the XIV Brazilian Symposium on Multimedia and the Web, 2008, pp. 390-392.
G, Salton; J. Allan and A. Singhal. “Automatic text decomposition and structuring”. Information Processing & Management, vol. 32(2), pp. 127–138, 1996.
G. Karypis. “CLUTO: A clustering toolkit”. Technical Report 02-017, College of Science and Engineering, University of Minnesota. Available: http://www.cs.umn.edu/research/technical_reports/view/02-017 [Jan. 14, 2015]
G.S. Day and P.J. Schoemaker. “Scanning the periphery”. Harvard Business Review , vol. 1(12), pp. 135-149, 2005.
H. I. Ansoff. “Managing Strategic Surprise by Response to Weak Signals”. California Management Review, vol. 18(2), pp. 21, 1975.
H. Lesca and N. Lesca. Weak Signals for Strategic Intelligence: Anticipation Tool for Managers . Hoboken, NJ: Wiley, 2011.
H. Lesca. “Veille stratégique: La méthode L.E.SCAnning®”. Editions EMS, 2003 180p.
J. Han and M. Kamber. Data Mining: Concepts and Techniques (2nd edition). Walthan, MA: Morgan Kaufmann, 2006.
J. Oliva; J.I. Serrano; M.D. del Castillo and A. Iglesias. “SyMSS: A syntax-based measure for short-text semantic similarity”. Data & Knowledge Engineering, vol. 70(4), pp. 390-405, 2011.
J.B. Lovins. “Development of a Stemming Algorithm”. Mechanical Translation and Computational Linguistics, vol. 11(1-2), pp. 22-31, 1968.
J.G. Walls; G.R. Widmeyer and O.A. El Sawy. “Building an Information system design theory for vigilant EIS”. Information System Research, vol. 3(1), pp. 36-59. 1992.
K. Rouibah and S. Ould-al. “PUZZLE: a concept and prototype for linking business intelligence to business strategy”. The Journal of Strategic Information Systems, vol. 11(2), pp. 133-152, 2002.
K. Xu; S. S. Liao; J. Li and Y. Song. “Mining comparative opinions from customer reviews for Competitive Intelligence”. Decision Support Systems, vol. 50(4), pp. 743–754, 2011.
L. Rokach and O. Maimon. “Clustering Methods” in Data Mining and Knowledge Discovery Handbook, 1st ed., O. Maimon and L. Rokach, Eds. New York: Springer, 2005, pp. 321- 352.
M. Holopainen and M. Toivonen. “Weak signals: Ansoff today”. Futures, vol. 44(3), pp.. 198–205, 2012.
M.-J. Shih; D.-R. Liu and M.-L. Hsu. “Discovering competitive intelligence by mining changes in patent trends”. Expert Systems with Applications, vol. 37(4), pp. 2882–2890, 2010.
M.-L. Caron-Fasan and R. Janissek-Muniz. "Análise de Informações de Inteligência Estratégica Antecipativa: Proposição de um Método, Caso Aplicado e Experiências". Revista de Administração da Universidade de São Paulo, vol. 39(3), pp 205-219, 2004.
M.A. Yunus; R. Zainuddin and N. Abdullah. “Visualizing Quran documents results by stemming semantic speech query”, in Proc. of the 2010 International Conference on User Science and Engineering (i-USEr), 2010, pp. 209-2013.
M.A.L. Dias. “Automatic extraction of Portuguesekey-words applied to dissertations and thesis in the engeneering area”. M.Sc. thesis, University of Campinas (Unicamp), Brazil, 2004.
N. Fanizzi; C. d’Amato and F. Esposito. “A Hierarchical Clustering Procedure for Semantically Annotated Resources”, in Proc. of the 10th Congress of the Italian Association for Artificial Intelligence, 2007, pp. 266-277.
N. Tabatabaei. “Detecting Weak Signals by Internet-Based Environmental Scanning ”. M.A. thesis, University of Waterloo, Canada, 2011.
N.A.A. Aziz; S.S. Salleh and D. Mohamad “Investigating Jaccard Distance Similarity Measurement Constriction on Handwritten Pen-based Input Digit”, in Proc. of the 2010 International Conference on Science and Social Research (CSSR), 2010, pp. 1181-1185.
P. Rossel. “Weak signals as a flexible framing space for enhanced management and decision making”. Technology Analysis & Strategic Management , vol. 21(3), pp. 307-320, 2009.
R. Baghel and R. Dhir. “Text Document Clustering Based on Frequent Concepts”, in Proc. of the 1st International Conference on Parallel, Distributed and Grid Computing (PDGC), 2010, pp. 366-371.
R. Feldman and J. Sanger. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data . Cambridge, MA: Cambridge University Press, 2006.
R. Janissek-Muniz; H. Lesca and H. Freitas. “Inteligência Estratégica Antecipativa e Coletiva para Tomada de Decisão”. Revista Organização em Contexto (Jul/Dez, 2007), pp. 92-118.
R. Janissek-Muniz; H. Lesca and H. Freitas. “Inteligência Estratégica Antecipativa e Coletiva para Tomada de Decisão”. Revista Organizações em Contexto, vol. 2(4), pp. 92- 118, 2005.
S. Delisle. “Towards a Better Integration of Data Mining and Decision Support via Computational Intelligence”, in Proc. of the 16th International Workshop on Database and Expert Systems Applications, 2005, pp. 720-724.
S. Haeckel. “Peripheral vision: Sensing and acting on weak signals making meaning out of apparent noise: The need for a new managerial framework”. Long Range Planning , vol. 37, pp. 181-189, 2004.
S. Mendonça; G. Cardoso and J. Caraça. “Some Notes on the Strategic Strength of Weak Signal Analysis”. Lini Working Papers no. 2. Available: http://www.lini- research.org/np4/working_papers [Jan. 13, 2015].
S.-C. Chu; J.F. Roddick and J.-S. Pan. “Improved search strategies and extensions to K- medoids -based clustering algorithms”. International Journal of Business Intelligence and Data Mining, vol. 3(2), pp. 212-231, 2008.
T. Kuosa. “Futures signals sense-making framework (FSSF): A start-up tool to analyse and categorise weak signals, wild cards, drivers, trends and other types of information”. Futures , vol. 42(1), pp. 42-48, 2010.
V.M. Orengo and C. Huyck. “A Stemming Algorithm for the Portuguese Language”, in Proc. of the 8 th International Symposium on String Processing and Information Retrieval (SPIRE), 2001, pp. 186-193.
Z. Xianjin and Y. Feng. “Research on the Acquirement of Enterprise Risk Competitive Intelligence Based on Data Mining”, in Proc. of the 5th International Conference on Wireless Communications, Networking and Mobile Computing (Wicom'09), 2009, pp. 1-4.
Mr. Antonio Leonardo Martins Moreira
Unicamp - Brazil
Mr. Thomas William Norio Hayashi
Unicamp - Brazil
Dr. Guilherme Palermo Coelho
Unicamp - Brazil
Dr. Ana Estela Antunes Silva
State University of Campinas - Brazil