Skip to ContentSkip to Navigation
About usNews and EventsNews articles

Fuzzy variants of prototype based clustering and classification algorithms

22 October 2012

PhD ceremony: Ms. T. Geweniger, 14.30 uur, Academiegebouw, Broerstraat 5, Groningen

Dissertation: Fuzzy variants of prototype based clustering and classification algorithms

Promotor(s): prof. M. Biehl, prof. T. Villmann

Faculty: Mathematics and Natural Sciences

Prototype based clustering and classification is a specific topic in the field of machine learning and artificial neural networks. Unsupervised clustering refers to grouping of data into sets of similar objects represented by prototypes and is among others applicable for explorative data mining, statistical data analysis, pattern recognition, and information retrieval. Supervised classification determines prototypes representing respective classes by taking a priori known class information into account. After successful prototype positioning new data samples can be classified accordingly. In practical applications data, which in fact belongs to different groups, i. e. clusters or classes, might be overlapping and therefore cannot be separated clearly. This overlapping of data is called fuzziness and refers to probabilistic or possibilistic assignments of data points to clusters or classes and has to be distinguished from fuzzy sets or fuzzy logic. Rather, it is learning with uncertainties. In this thesis some supervised and unsupervised methods -- in particular c- Means, Learning Vector Quantization, Self Organizing Maps, Neural Gas, and Affinity Propagation -- are modified or extended to incorporate this kind of fuzziness. Although some of the mentioned methods already have variants dealing with fuzzy data, the now proposed modifications concern different further aspects like clustering median data, using divergences as dissimilarity measure, or learning relevances. Further, to evaluate a fuzzy classification or cluster solution different measures are used. One of them, the Fleiss‘ Kappa Index is modified to be also applicable to fuzzy solutions.

Last modified:15 September 2017 3.42 p.m.
printView this page in: Nederlands

More news

  • 17 October 2019

    iGEM 2019: Team Groningen develops a biological QR-code

    The team developed a protocol to share delicate information via a biological QR-code.

  • 14 October 2019

    Demonstrating that it can be done

    Moniek Tromp and Marleen Kamperman share a lot in common. Both are young professors of chemistry at the Zernike Institute for Advanced Materials (ZIAM).Both joined the UG in 2018. Both are engaged in developing new materials. Both enjoy promoting science...

  • 11 October 2019

    Down Under with Top Dutch (update)

    After two years of hard work, the Groningen Top Dutch Solar Racing team has arrived in Australia. The team consists of students of the Hanze University of Applied Sciences, University of Groningen, and secondary vocational education (MBO) and is currently...