Contact:
Room: 523
Position:
Associate Professor
Classes:
Systemy rekomendacyjne lab
Systemy rekomendacyjne wyk
Sztuczna inteligencja lab
Sztuczna inteligencja wyk
Metody przetwarzania języka naturalnego lab
Metody przetwarzania języka naturalnego wyk
PhD DSc ProfPCz
Piotr Duda
Papers (62)
2024 (3)
Urbanczyk A., Kucaba K., Wojtulewicz M., Kisiel-Dorohinicki M., Rutkowski L., Duda P., Kacprzyk J., Yao X., Chong S.Y., Byrski A., (μ +λ) Evolution Strategy with Socio-Cognitive Mutation. (0)
(μ +λ) Evolution Strategy with Socio-Cognitive Mutation
, (μ +λ) Evolution Strategy with Socio-Cognitive Mutation, Journal of Automation, Mobile Robotics and Intelligent Systems, 18, 18, 1-11, 2024, Cites: 0
Duda P., Wojtulewicz M., Rutkowski L., Accelerating deep neural network learning using data stream methodology. (1)
Accelerating deep neural network learning using data stream methodology
, Accelerating deep neural network learning using data stream methodology, Information Sciences, 669, 669, 2024, Cites: 1
Rutkowska D., Duda P., Cao J., Jaworski M., Kisiel-Dorohinicki M., Tao D., Rutkowski L., Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring. (0)
Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring
, Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring, Applied Soft Computing, 161, 161, 2024, Cites: 02023 (4)
Woldan P., Duda P., Cader A., Laktionov I., A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps. (5)
A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps
, A New Approach to Image-Based Recommender Systems with the Application of Heatmaps Maps, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 63-72, 2023, Cites: 5
Zalasinski M., Duda P., Lota S., Cpalka K., Dynamic Signature Verification Using Selected Regions. (1)
Dynamic Signature Verification Using Selected Regions
, Dynamic Signature Verification Using Selected Regions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 388-397, 2023, Cites: 1
Duda P., Wojtulewicz M., Rutkowski L., The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach. (0)
The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach
, The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 46-55, 2023, Cites: 0
Rutkowska D., Duda P., Cao J., Rutkowski L., Byrski A., Jaworski M., Tao D., The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks. (6)
The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks
, The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks, Information Sciences, 631, 631, 346-368, 2023, Cites: 62022 (1)
Chen G.Y., Krzyzak A., Duda P., Cader A., Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain. (4)
Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain
, Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 169-180, 2022, Cites: 42021 (1)
Duda P., Rutkowski L., Woldan P., Najgebauer P., The Streaming Approach to Training Restricted Boltzmann Machines. (0)
The Streaming Approach to Training Restricted Boltzmann Machines
, The Streaming Approach to Training Restricted Boltzmann Machines, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 308-317, 2021, Cites: 02020 (20)
Rutkowski L., Jaworski M., Duda P., Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks. (1)
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
, Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks, Studies in Big Data, 56, 56, 173-244, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., The General Procedure of Ensembles Construction in Data Stream Scenarios. (0)
The General Procedure of Ensembles Construction in Data Stream Scenarios
, The General Procedure of Ensembles Construction in Data Stream Scenarios, Studies in Big Data, 56, 56, 281-286, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Classification. (0)
Classification
, Classification, Studies in Big Data, 56, 56, 287-308, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Final Remarks and Challenging Problems. (0)
Final Remarks and Challenging Problems
, Final Remarks and Challenging Problems, Studies in Big Data, 56, 56, 323-327, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Regression. (0)
Regression
, Regression, Studies in Big Data, 56, 56, 309-322, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Splitting Criteria with the Bias Term. (0)
Splitting Criteria with the Bias Term
, Splitting Criteria with the Bias Term, Studies in Big Data, 56, 56, 83-89, 2020, Cites: 0
Duda P., Przybyszewski K., Wang L., A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment. (7)
A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment
, A Novel Drift Detection Algorithm Based on Features' Importance Analysis in a Data Streams Environment, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 287-298, 2020, Cites: 7
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining. (7)
Decision Trees in Data Stream Mining
, Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 2020, Cites: 7
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Data Stream Mining. (18)
Basic Concepts of Data Stream Mining
, Basic Concepts of Data Stream Mining, Studies in Big Data, 56, 56, 13-33, 2020, Cites: 18
Rutkowski L., Jaworski M., Duda P., Introduction and Overview of the Main Results of the Book. (1)
Introduction and Overview of the Main Results of the Book
, Introduction and Overview of the Main Results of the Book, Studies in Big Data, 56, 56, 1-10, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure. (2)
Misclassification Error Impurity Measure
, Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 2
Duda P., Jaworski M., Cader A., Wang L., On Training Deep Neural Networks Using a Streaming Approach. (24)
On Training Deep Neural Networks Using a Streaming Approach
, On Training Deep Neural Networks Using a Streaming Approach, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 15-26, 2020, Cites: 24
Duda P., Rutkowski L., Jaworski M., Rutkowska D., On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification. (32)
On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification
, On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification, IEEE Transactions on Cybernetics, 50, 50, 1683-1696, 2020, Cites: 32
Woldan P., Duda P., Hayashi Y., Visual Hybrid Recommendation Systems Based on the Content-Based Filtering. (3)
Visual Hybrid Recommendation Systems Based on the Content-Based Filtering
, Visual Hybrid Recommendation Systems Based on the Content-Based Filtering, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 455-465, 2020, Cites: 3
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria. (1)
Hybrid Splitting Criteria
, Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., General Non-parametric Learning Procedure for Tracking Concept Drift. (1)
General Non-parametric Learning Procedure for Tracking Concept Drift
, General Non-parametric Learning Procedure for Tracking Concept Drift, Studies in Big Data, 56, 56, 155-172, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Probabilistic Neural Networks. (0)
Basic Concepts of Probabilistic Neural Networks
, Basic Concepts of Probabilistic Neural Networks, Studies in Big Data, 56, 56, 117-154, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Splitting Criteria Based on the McDiarmid’s Theorem. (0)
Splitting Criteria Based on the McDiarmid’s Theorem
, Splitting Criteria Based on the McDiarmid’s Theorem, Studies in Big Data, 56, 56, 51-62, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification. (2)
Probabilistic Neural Networks for the Streaming Data Classification
, Probabilistic Neural Networks for the Streaming Data Classification, Studies in Big Data, 56, 56, 245-277, 2020, Cites: 2
Duda P., Wang L., On a Streaming Approach for Training Denoising Auto-encoders. (0)
On a Streaming Approach for Training Denoising Auto-encoders
, On a Streaming Approach for Training Denoising Auto-encoders, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 315-324, 2020, Cites: 02019 (3)
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)). (0)
Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028))
, Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)), Information Sciences, 477, 477, 545, 2019, Cites: 0
Jaworski M., Rutkowski L., Duda P., Cader A., Resource-aware data stream mining using the restricted boltzmann machine. (4)
Resource-aware data stream mining using the restricted boltzmann machine
, Resource-aware data stream mining using the restricted boltzmann machine, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 384-396, 2019, Cites: 4
Jaworski M., Duda P., Rutkowska D., Rutkowski L., On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine. (2)
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine
, On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine, Communications in Computer and Information Science, 1143 CCIS, 1143 CCIS, 347-354, 2019, Cites: 22018 (6)
Duda P., Jaworski M., Rutkowski L., Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks. (24)
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences, 460-461, 460-461, 497-518, 2018, Cites: 24
Jaworski M., Duda P., Rutkowski L., Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine. (13)
Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine
, Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine, Proceedings of the International Joint Conference on Neural Networks, 2018-July, 2018-July, 2018, Cites: 13
Duda P., Jaworski M., Rutkowski L., Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. (31)
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
, Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks, International Journal of Neural Systems, 28, 28, 2018, Cites: 31
Jaworski M., Duda P., Rutkowski L., New Splitting Criteria for Decision Trees in Stationary Data Streams. (89)
New Splitting Criteria for Decision Trees in Stationary Data Streams
, New Splitting Criteria for Decision Trees in Stationary Data Streams, IEEE Transactions on Neural Networks and Learning Systems, 29, 29, 2516-2529, 2018, Cites: 89
Duda P., On ensemble components selection in data streams scenario with gradual concept-drift. (3)
On ensemble components selection in data streams scenario with gradual concept-drift
, On ensemble components selection in data streams scenario with gradual concept-drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 311-320, 2018, Cites: 3
Duda P., Jaworski M., Rutkowski L., Online grnn-based ensembles for regression on evolving data streams. (6)
Online grnn-based ensembles for regression on evolving data streams
, Online grnn-based ensembles for regression on evolving data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10878 LNCS, 10878 LNCS, 221-228, 2018, Cites: 62017 (4)
Jaworski M., Duda P., Rutkowski L., On applying the Restricted Boltzmann Machine to active concept drift detection. (22)
On applying the Restricted Boltzmann Machine to active concept drift detection
, On applying the Restricted Boltzmann Machine to active concept drift detection, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-8, 2017, Cites: 22
Duda P., Jaworski M., Rutkowski L., On ensemble components selection in data streams scenario with reoccurring concept-drift. (16)
On ensemble components selection in data streams scenario with reoccurring concept-drift
, On ensemble components selection in data streams scenario with reoccurring concept-drift, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-7, 2017, Cites: 16
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?. (65)
How to adjust an ensemble size in stream data mining?
, How to adjust an ensemble size in stream data mining?, Information Sciences, 381, 381, 46-54, 2017, Cites: 65
Jaworski M., Duda P., Rutkowski L., Najgebauer P., Pawlak M., Heuristic regression function estimation methods for data streams with concept drift. (10)
Heuristic regression function estimation methods for data streams with concept drift
, Heuristic regression function estimation methods for data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 726-737, 2017, Cites: 102016 (3)
Duda P., Jaworski M., Pietruczuk L., Korytkowski M., Gabryel M., Scherer R., On the application of orthogonal series density estimation for image classification based on feature description. (1)
On the application of orthogonal series density estimation for image classification based on feature description
, On the application of orthogonal series density estimation for image classification based on feature description, Advances in Intelligent Systems and Computing, 364, 364, 529-540, 2016, Cites: 1
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., A method for automatic adjustment of ensemble size in stream data mining. (18)
A method for automatic adjustment of ensemble size in stream data mining
, A method for automatic adjustment of ensemble size in stream data mining, Proceedings of the International Joint Conference on Neural Networks, 2016-October, 2016-October, 9-15, 2016, Cites: 18
Duda P., Pietruczuk L., Jaworski M., Krzyzak A., On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions. (2)
On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions
, On the Cesàro-means-based orthogonal series approach to learning time-varying regression functions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 37-48, 2016, Cites: 22015 (1)
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., A new method for data stream mining based on the misclassification error. (104)
A new method for data stream mining based on the misclassification error
, A new method for data stream mining based on the misclassification error, IEEE Transactions on Neural Networks and Learning Systems, 26, 26, 1048-1059, 2015, Cites: 1042014 (4)
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., The CART decision tree for mining data streams. (270)
The CART decision tree for mining data streams
, The CART decision tree for mining data streams, Information Sciences, 266, 266, 1-15, 2014, Cites: 270
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., The Parzen kernel approach to learning in non-stationary environment. (12)
The Parzen kernel approach to learning in non-stationary environment
, The Parzen kernel approach to learning in non-stationary environment, Proceedings of the International Joint Conference on Neural Networks, 3319-3323, 2014, Cites: 12
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., Decision trees for mining data streams based on the gaussian approximation. (144)
Decision trees for mining data streams based on the gaussian approximation
, Decision trees for mining data streams based on the gaussian approximation, IEEE Transactions on Knowledge and Data Engineering, 26, 26, 108-119, 2014, Cites: 144
Duda P., Jaworski M., Pietruczuk L., Rutkowski L., A novel application of Hoeffding's inequality to decision trees construction for data streams. (16)
A novel application of Hoeffding's inequality to decision trees construction for data streams
, A novel application of Hoeffding's inequality to decision trees construction for data streams, Proceedings of the International Joint Conference on Neural Networks, 3324-3330, 2014, Cites: 162013 (2)
Pietruczuk L., Duda P., Jaworski M., Adaptation of decision trees for handling concept drift. (22)
Adaptation of decision trees for handling concept drift
, Adaptation of decision trees for handling concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 459-473, 2013, Cites: 22
Rutkowski L., Pietruczuk L., Duda P., Jaworski M., Decision trees for mining data streams based on the mcdiarmid's bound. (168)
Decision trees for mining data streams based on the mcdiarmid's bound
, Decision trees for mining data streams based on the mcdiarmid's bound, IEEE Transactions on Knowledge and Data Engineering, 25, 25, 1272-1279, 2013, Cites: 1682012 (10)
Duda P., Jaworski M., Pietruczuk L., On pre-processing algorithms for data stream. (18)
On pre-processing algorithms for data stream
, On pre-processing algorithms for data stream, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 56-63, 2012, Cites: 18
Er M.J., Duda P., On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. (14)
On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment
, On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 443-450, 2012, Cites: 14
Er M.J., Duda P., On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment. (0)
On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment
, On the uniform convergence of the orthogonal series-type kernel regression neural networks in a time-varying environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 39-46, 2012, Cites: 0
Duda P., Zurada J.M., On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise. (1)
On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise
, On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 435-442, 2012, Cites: 1
Duda P., Hayashi Y., On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment. (0)
On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment
, On the weak convergence of the recursive orthogonal series-type kernel probabilistic neural networks in a time-varying environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7203 LNCS, 7203 LNCS, 427-434, 2012, Cites: 0
Duda P., Korytkowski M., On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise. (0)
On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise
, On the strong convergence of the recursive orthogonal series-type kernel probabilistic neural networks handling time-varying noise, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 55-62, 2012, Cites: 0
Pietruczuk L., Duda P., Jaworski M., A new fuzzy classifier for data streams. (18)
A new fuzzy classifier for data streams
, A new fuzzy classifier for data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 318-324, 2012, Cites: 18
Jaworski M., Duda P., Pietruczuk L., On fuzzy clustering of data streams with concept drift. (18)
On fuzzy clustering of data streams with concept drift
, On fuzzy clustering of data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 82-91, 2012, Cites: 18
Duda P., Hayashi Y., Jaworski M., On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment. (17)
On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment
, On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 47-54, 2012, Cites: 17
Jaworski M., Pietruczuk L., Duda P., On resources optimization in fuzzy clustering of data streams. (17)
