Probability κ-means clustering for neural network architecture

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Cluster analysis indices are aimed at classifying the elements used to estimate the quality of the categories based on their similarity. It is a challenging task because, with the same data set, there may be many partitions that fit natural groupings of a given data set. Applications of clustering can include pattern recognition, image analysis and information retrieval. We propose an approach based on the κ-means concept that clustering centers more often have a higher density than their neighbors: then we use a probability κ-means algorithm to achieve fuzzy clustering in continuous form over a relatively large distance from other points with higher densities. Further, in order to follow the mainstream neural network architecture, we define a favorable activation function and corresponding loss function for the clustering iteration. Our method come from the basis of a clustering procedure in which the number of clusters arises intuitively and clusters are achieved regardless of the high dimensions. We demonstrate the result of complete algorithm on several clustering test cases.

Original languageEnglish
Title of host publicationICAAI 2019 - 2019 the 3rd International Conference on Advances in Artificial Intelligence
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450372534
Publication statusPublished - 26 Oct 2019
Event3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 - Istanbul, Turkey
Duration: 26 Oct 201928 Oct 2019

Publication series

NameACM International Conference Proceeding Series


Conference3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019


  • Clustering Analysis
  • Neural Network Architecture
  • Probability κ-means


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