Germán Sánchez-Hernández

Ranking and selection of unsupervised learning marketing segmentation

This paper addresses the problem of choosing the most appropriate classification from a given set of classifications of a set of patterns. This is a relevant topic on unsupervised systems and clustering analysis because different classifications can in general be obtained from the same data set. The provided methodology is based on five fuzzy criteria which are aggregated using an Ordered Weighted Averaging (OWA) operator. To this end, a novel multi-criteria decision making (MCDM) system is defined, which assesses the degree up to which each criterion is met by all classifications. The corresponding single evaluations are then proposed to be aggregated into a collective one by means of an OWA operator guided by a fuzzy linguistic quantifier, which is used to implement the concept of fuzzy majority in the selection process. This new methodology is applied to a real marketing case based on a business to business (B2B) environment to help marketing experts during the segmentation process. As a result, a segmentation containing three segments consisting of 35, 98 and 127 points of sale respectively is selected to be the most suitable to endorse marketing strategies of the firm. Finally, an analysis of the managerial implications of the proposed methodology solution is provided.

InsERT: The inspirational expert recommender tool

The continued growth in enterprise social networks is fueled by the need to enable productivity and innovation. Reducing the constraints to communication and knowledge sharing of a globally distributed workforce, will facilitate the workflow. People finder systems are one of the main solutions in enterprise social networks which are reducing these constraints leading to time and cost savings. Finding expertise efficiently helps organizations to unlock knowledge within the enterprise, solve problems, and identify collaborators. However, the following challenges still exist: validating expertise, determining responsiveness and accessibility, and managing expert profiles. In this paper, we propose the fuzzy OWA technique as a novel approach to ranking candidates in expertise search. We consider its application in open innovation intermediaries where the search for partners and expertise is at the center of the business model. In addition, we demonstrate its application in a software tool (InsERT) as part of a larger enterprise social network implementation.

Facilitating Creativity in Collaborative Work with Computational Intelligence Software

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking-from the combined knowledge and abilities of people with diverse perspectives-contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015.

Consensus in innovation contest categorisation by means of fuzzy partitions

Graphical abstractDisplay Omitted Consensus decision-making is fuzzy by nature, yet fuzzy consensus decision-making in a medium to large number of decisions is not widely used since it demands additional information that requires extra decision-maker effort. Consensus decision-making rests on properly measured agreement. This paper proposes a fuzzy measure of agreement through fuzzy kappa based on fuzzy partitions. These fuzzy partitions enable decision-makers to assess their decisions with a degree of confidence. A fuzzy partition is built for each decision-maker considering his/her confidence degrees when categorising a set of alternatives or solutions. This enables decision-makers to more easily capture the fuzzy nature of the decision. In addition, this paper presents a real-life experiment based on a innovation contest to show the feasibility of using confidence degrees in real-life applications compared to traditional consensus decision-making. The results suggest that the use of confidence degrees improves the level of agreement in the consensus decision-making process through fuzzy kappa coefficients, and it also improves the level of agreement in the consensus decision-making process.

Evaluating student-internship fit using fuzzy linguistic terms and a fuzzy OWA operator

Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidates interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a students interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.