<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This paper introduces a method of data clustering that is based on linguistically specified rules, similar to those applied by a human visually fulfilling a task. The method endeavors to follow these remarkable capabilities of intelligent beings. Even for most complicated data patterns a human is capable of accomplishing the clustering process using relatively simple rules. His/her way of clustering is a sequential search for new structures in the data and new prototypes with the use of the following linguistic rule: search for prototypes in regions of extremely high data densities and immensely far from the previously found ones. Then, after this search has been completed, the respective data have to be assigned to any of the clusters whose nuclei (prototypes) have been found. A human again uses a simple linguistic rule: data from regions with similar densities, which are located exceedingly close to each other, should belong to the same cluster. The goal of this work is to prove experimentally that such simple linguistic rules can result in a clustering method that is competitive with the most effective methods known from the literature on the subject. A linguistic formulation of a validity index for determination of the number of clusters is also presented. Finally, an extensive experimental analysis of benchmark datasets is performed to demonstrate the validity of the clustering approach introduced. Its competitiveness with the state-of-the-art solutions is also shown.</p></abstract>

This paper introduces a method of data clustering that is based on linguistically specified rules, similar to those applied by a human visually fulfilling a task. The method endeavors to follow these remarkable capabilities of intelligent beings. Even for most complicated data patterns a human is capable of accomplishing the clustering process using relatively simple rules. His/her way of clustering is a sequential search for new structures in the data and new prototypes with the use of the following linguistic rule: search for prototypes in regions of extremely high data densities and immensely far from the previously found ones. Then, after this search has been completed, the respective data have to be assigned to any of the clusters whose nuclei (prototypes) have been found. A human again uses a simple linguistic rule: data from regions with similar densities, which are located exceedingly close to each other, should belong to the same cluster. The goal of this work is to prove experimentally that such simple linguistic rules can result in a clustering method that is competitive with the most effective methods known from the literature on the subject. A linguistic formulation of a validity index for determination of the number of clusters is also presented. Finally, an extensive experimental analysis of benchmark datasets is performed to demonstrate the validity of the clustering approach introduced. Its competitiveness with the state-of-the-art solutions is also shown.

Linguistically Defined Clustering of Data

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Linguistically Defined Clustering of Data"}

This paper introduces a method of data clustering that is based on linguistically specified rules, similar to those applied by a human visually fulfilling a...

Linguistically Defined Clustering of Data

Online veröffentlicht: 03. Okt. 2018

Seitenbereich: 545 - 557

Eingereicht: 17. Juli 2017

Akzeptiert: 16. Apr. 2018

DOI: https://doi.org/10.2478/amcs-2018-0042

Schlüsselwörter
clustering, possibility theory, linguistic rules, data analysis

© 2018 Jacek M. Leski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Linguistically Defined Clustering of Data

Online veröffentlicht: 03. Okt. 2018

Seitenbereich: 545 - 557

Eingereicht: 17. Juli 2017

Akzeptiert: 16. Apr. 2018

DOI: https://doi.org/10.2478/amcs-2018-0042

Schlüsselwörterclustering, possibility theory, linguistic rules, data analysis

© 2018 Jacek M. Leski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
clustering, possibility theory, linguistic rules, data analysis