It is required for an organization, before successfully applying a Knowledge Management (KM) methodology, to develop and implement a knowledge infrastructure, consisting of people, organizational and technological systems. Up to now, few approaches have been proposed for such technological systems supporting KM in organizations. Present paper advances previous work by proposing neural projection models for the analysis of the KM status of companies from two different industrial sectors. Exploratory methods are applied to real-life case studies to know and understand the structure of KM data. Subsequently, the application of such models generates meaningful conclusions that allow experts to diagnose KM from two different points of view: companies on the one hand and industrial sectors on the other hand.
This is a preview of subscription content, log in to check access.
Herrero Á, Corchado E, Sáiz L, Abraham A (2010) DIPKIP: A connectionist knowledge management system to identify knowledge deficits in practical cases. Comput Intell 26:26–56CrossRefzbMATHMathSciNetGoogle Scholar
Durst S, Edvardsson IR (2012) Knowledge management in SMEs: a literature review. J Knowl Manag 16:879–903CrossRefGoogle Scholar
Levy M (2011) Knowledge retention: minimizing organizational business loss. J Knowl Manag 15:582–600CrossRefGoogle Scholar
Herrero Á, Corchado E, Jiménez A (2011) Unsupervised neural models for country and political risk analysis. Expert Syst Appl 38:13641–13661Google Scholar
Herrero Á, Corchado E, Gastaldo P, Zunino R (2009) Neural projection techniques for the visual inspection of network traffic. Neurocomputing 72:3649–3658CrossRefGoogle Scholar
Friedman JH, Tukey JW (1974) A projection pursuit algorithm for exploratory data-analysis. IEEE Trans Comput 23:881–890CrossRefzbMATHGoogle Scholar
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417–444CrossRefGoogle Scholar
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2:559–572CrossRefGoogle Scholar
Seung HS, Socci ND, Lee D (1998) The Rectified Gaussian Distribution. Adv Neural Inf Process Syst 10:350–356Google Scholar