The Complete Gradient Clustering Algorithm: properties in practical applications

Kulczycki, Piotr; Charytanowicz, Małgorzata; Kowalski, Piotr A.; Łukasik, Szymon

doi:10.1080/02664763.2011.644526

Cited by 25 publications

(3 citation statements)

References 22 publications

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“…In particular -in [10] -Celinski-Harabasz, Davies-Bould in and Silhouette Value Indexes as clustering variants has been utilized to validate the cluster division criteria. Furthermore -in [2,15] -for clustering purposes the Complete Gradient Clustering heuristic algorithm, based on the density of the data is introduced, and in this case is applied to a real-world data set of grains [3], as well as to other benchmark data sets.…”

Section: Optimization Inspired By Nature As a Tool For Eda -A Survey mentioning

confidence: 99%

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Kowalski¹,

Lłukasik²,

Kulczycki³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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This study contains a brief presentation of the basic tasks for Exploratory Data Analysis (EDA), namely: classification, clustering, reduction of data dimensionality and number of data instances as well as detection of outliers. Herein, solutions to the aforementioned problems incorporating a wide range of computational intelligence algorithms, in particular procedures based on collective intelligence, are under consideration. Furthermore, the combination of metaheuristic algorithms with basic EDA procedures applied and verified within many domains of science, technology and engineering are being presented.

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Section: Optimization Inspired By Nature As a Tool For Eda -A Survey mentioning

confidence: 99%

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Kowalski¹,

Lłukasik²,

Kulczycki³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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“…In a different situation, the clustering algorithm must be set out in addition to assigning the elements of the data set to individual classes. As these, above all, determine the number of classes under consideration, the Complete Gradient Algorithm (CGA) [ 17 , 21 , 22 ] is strongly recommended. A very big positive for utilizing this approach is the existing possibility of applying statistical kernel estimation methods—the same methodology as in the IPNN case.…”

Section: Complete Neural Algorithm For the Classification Of Intervalmentioning

confidence: 99%

Interval probabilistic neural network

Kowalski

Kulczycki

2015

Neural Comput & Applic

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Automated classification systems have allowed for the rapid development of exploratory data analysis. Such systems increase the independence of human intervention in obtaining the analysis results, especially when inaccurate information is under consideration. The aim of this paper is to present a novel approach, a neural networking, for use in classifying interval information. As presented, neural methodology is a generalization of probabilistic neural network for interval data processing. The simple structure of this neural classification algorithm makes it applicable for research purposes. The procedure is based on the Bayes approach, ensuring minimal potential losses with regard to that which comes about through classification errors. In this article, the topological structure of the network and the learning process are described in detail. Of note, the correctness of the procedure proposed here has been verified by way of numerical tests. These tests include examples of both synthetic data, as well as benchmark instances. The results of numerical verification, carried out for different shapes of data sets, as well as a comparative analysis with other methods of similar conditioning, have validated both the concept presented here and its positive features.

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“…This partition is achieved through developing a function which assigns individual elements of the data collection into each subset. This technique has been applied to a wide range of problems, including various technical tasks [1], robotics [2] and control approaches [3], to aspects of economics [4], as well as to many agricultural issues [5].…”

Section: Introductionmentioning

confidence: 99%

Clustering based on the Krill Herd Algorithm with Selected Validity Measures

Kowalski

Łukasik

Charytanowicz

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-This paper describes a new approach to metaheuristic-based data clustering by means of Krill Herd Algorithm (KHA). In this work, KHA is used to find centres of the cluster groups. Moreover, the number of clusters is set up at the beginning of the procedure, and during the subsequent iterations of the optimization algorithm, particular solutions are evaluated by selected validity criteria. The proposed clustering algorithm has been numerically verified using twelve data sets taken from the UCI Machine Learning Repository. Additionally, all cases of clustering were compared with the most popular method of k-means, through the Rand Index being applied as a validity measure.

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The Complete Gradient Clustering Algorithm: properties in practical applications

Cited by 25 publications

References 22 publications

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Interval probabilistic neural network

Clustering based on the Krill Herd Algorithm with Selected Validity Measures

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