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Intelligent synthetic composite indicators with application

  • Methodologies and Application
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Abstract

This article is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (ISCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and then Fuzzy c-means is employed to form the new composite indicators. A fully worked application is presented. The application uses Information and Communication Technology real variables to form a new unified ICT index, illustrating the method of construction for ISCI. The weighting and aggregation results obtained were compared against Principal Component Analysis, Factor Analysis and the Geometric mean to weight and aggregate synthetic composite indicators. This study also compares and contrasts two special Fuzzy c-means techniques that is, the Optimal Completion Strategy and the Nearest Prototype Strategy to impute missing values. The results are compared against statistical imputation techniques. The validity and robustness of all techniques are evaluated using Monte Carlo simulation. The results obtained suggest a novel, intelligent and non-biased method of building future composite indicators.

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Notes

  1. Statistica 10—Data Mining software was used to conduct the SFC web mining.

  2. NVivo 10 software is used to carry out the text search and match. NVivo 10, uses Lucene text search engine to handle text search and match queries in any language (encoding ASCI and UTF-8), and finds words based on the Wagner–Fischer dynamic programming.

  3. NVivo10: http://www.qsrinternational.com/products_nvivo.aspx.

  4. The WordNet thesaurus built in NVivo 10 software is utilised to search and include the “synonyms” and “specialisation” of the used keywords.

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Authors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Ahmad Al Shami & Ahmad Lotfi

  2. Nottingham Business School, Nottingham Trent University, Nottingham, NG1 4BU, UK

    Simeon Coleman

Authors
  1. Ahmad Al Shami
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  2. Ahmad Lotfi
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  3. Simeon Coleman
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Correspondence to Ahmad Al Shami.

Additional information

Communicated by G. Acampora.

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Al Shami, A., Lotfi, A. & Coleman, S. Intelligent synthetic composite indicators with application. Soft Comput 17, 2349–2364 (2013). https://doi.org/10.1007/s00500-013-1098-3

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