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Journal articles on the topic 'Frequency-clustering'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Banerjee, Arko, Suvendu Chandan Nayak, and Chhabi Rani Panigrahi. "Weighted Clustering Ensemble with Base Clustering Frequency and Diversity." International Journal of Electronics Engineering and Applications 10, no. 2 (2021): 41–50. http://dx.doi.org/10.30696/ijeea.x.ii.2022.41-50.

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2

Xue, Yi, and Ramazan Gençay. "Trading frequency and volatility clustering." Journal of Banking & Finance 36, no. 3 (2012): 760–73. http://dx.doi.org/10.1016/j.jbankfin.2011.09.008.

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3

SUN, Shuping, Zhongwei JIANG, and Haibin WANG. "1204 Heart Sound Clustering Method Using Time-Frequency Distribution Energy." Proceedings of Conference of Chugoku-Shikoku Branch 2010.48 (2010): 365–66. http://dx.doi.org/10.1299/jsmecs.2010.48.365.

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4

Shumway, Robert H. "Time-frequency clustering and discriminant analysis." Statistics & Probability Letters 63, no. 3 (2003): 307–14. http://dx.doi.org/10.1016/s0167-7152(03)00095-6.

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5

Rahozin, D. V., and A. Yu Doroshenko. "Low frequency signal classification using clustering methods." PROBLEMS IN PROGRAMMING, no. 1 (January 2024): 48–56. http://dx.doi.org/10.15407/pp2024.01.048.

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The article considers the problem of low frequency signal classification, as sound or vibration pattern footprints may describe types of objects very well. In cases of a priori absence of object signal pattern information, the unsupervised learning methods based on clustering looks good enough for classification, and outperform neural net-based methods in case of limited power envelope. We have used big real-world sound and vibration data set to check several clustering methods (K-Means, OPTICS) for classification without any a priori data and have got good enough results. The article consider

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6

Adhikari, Animesh. "Clustering local frequency items in multiple databases." Information Sciences 237 (July 2013): 221–41. http://dx.doi.org/10.1016/j.ins.2013.02.043.

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7

Bao, Jun Peng, Jun Yi Shen, Xiao Dong Liu, and Hai Yan Liu. "The heavy frequency vector-based text clustering." International Journal of Business Intelligence and Data Mining 1, no. 1 (2005): 42. http://dx.doi.org/10.1504/ijbidm.2005.007317.

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8

Aki, Hazar Cenk, M. Erturk, and Huseyin Arslan. "Fractional Reuse Partitioning Schemes for Overlay Cellular Architectures." International Journal of Interdisciplinary Telecommunications and Networking 2, no. 4 (2010): 15–29. http://dx.doi.org/10.4018/jitn.2010100102.

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In this paper, the authors propose three partitioning schemes for adaptive clustering with fractional frequency reuse namely maximal fractional frequency reuse partitioning (MFRP), optimal fractional reuse partitioning (OFRP), and GoS-oriented frequency reuse partitioning. The authors propose that an overlaid cellular clustering scheme, which uses adaptive fractional frequency reuse factors, would provide a better capacity by exploiting the high level of signal to interference ratio (SIR). The proposed methods are studied via simulations and the results show that the adaptive clustering with d

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Al-Obaydy, Wasseem N. Ibrahem, Hala A. Hashim, Yassen AbdelKhaleq Najm, and Ahmed Adeeb Jalal. "Document classification using term frequency-inverse document frequency and K-means clustering." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1517. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1517-1524.

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include

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Al-Obaydy, Wasseem N. Ibrahem, Hala A. Hashim, Yassen AbdulKhaleq Najm, and Ahmed Adeeb Jalal. "Document classification using term frequency-inverse document frequency and K-means clustering." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1517–24. https://doi.org/10.11591/ijeecs.v27.i3.pp1517-1524.

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include

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11

Fokianos, Konstantinos, and Vasilis J. Promponas. "Biological applications of time series frequency domain clustering." Journal of Time Series Analysis 33, no. 5 (2011): 744–56. http://dx.doi.org/10.1111/j.1467-9892.2011.00758.x.

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12

Lopes da Silva, Fernando H., Jaime Parra Gomez, Dimitri N. Velis, and Stiliyan Kalitzin. "Phase Clustering of High Frequency EEG: MEG Components." Clinical EEG and Neuroscience 36, no. 4 (2005): 306–10. http://dx.doi.org/10.1177/155005940503600410.

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The study of phase consistency of high frequency EEG/MEG components can reveal properties of neuronal networks that are informative about their excitability state. The clue is that these properties are easier to put in evidence when the response of the neuronal networks is evoked by an adequate stimulation paradigm. The latter may be considered a probe of neuronal excitability state capable of revealing hidden information contained in the phase structure of neuronal activities. In this context the high frequency band components appear to be the most reactive signals.

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13

Scheunders, P., and S. De Backer. "High-dimensional clustering using frequency sensitive competitive learning." Pattern Recognition 32, no. 2 (1999): 193–202. http://dx.doi.org/10.1016/s0031-3203(98)00136-8.

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14

Cui, Hongyan, Kuo Zhang, Yajun Fang, Stanislav Sobolevsky, Carlo Ratti, and Berthold K. P. Horn. "A Clustering Validity Index Based on Pairing Frequency." IEEE Access 5 (2017): 24884–94. http://dx.doi.org/10.1109/access.2017.2743985.

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15

Wang, Dan Dan, Yu Zhou, Qing Wei Ye, and Xiao Dong Wang. "The Spectrum Segmentation Algorithm of Multimode Vibration Signal Based on Spectral Clustering." Applied Mechanics and Materials 121-126 (October 2011): 2372–76. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2372.

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The mode peaks in frequency domain of vibration signal are strongly interfered by strong noise, causing the inaccuracy mode parameters. According to this situation, this paper comes up with the thought of mode-peak segmentation based on the spectral clustering algorithm. First, according to the concept of wave packet, the amplitude-frequency of vibration signal is divided into wave packets. Taking each wave packet as a sample of clustering algorithm, the spectral clustering algorithm is used to classify these wave packets. The amplitude-frequency curve of a mode peak becomes a big wave packet

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16

Obidallah, Waeal J., Bijan Raahemi, and Waleed Rashideh. "Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques." Data 7, no. 5 (2022): 57. http://dx.doi.org/10.3390/data7050057.

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We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services

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17

Peng, Furong, Jiachen Luo, Xuan Lu, Sheng Wang, and Feijiang Li. "Cross-Domain Contrastive Learning for Time Series Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8921–29. http://dx.doi.org/10.1609/aaai.v38i8.28740.

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Most deep learning-based time series clustering models concentrate on data representation in a separate process from clustering. This leads to that clustering loss cannot guide feature extraction. Moreover, most methods solely analyze data from the temporal domain, disregarding the potential within the frequency domain. To address these challenges, we introduce a novel end-to-end Cross-Domain Contrastive learning model for time series Clustering (CDCC). Firstly, it integrates the clustering process and feature extraction using contrastive constraints at both cluster-level and instance-level. S

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18

Mujilahwati, Siti. "Kombinasi Algoritma Data Reduksi untuk Optimalisasi Dokumen Cluster." Jurnal Eksplora Informatika 12, no. 2 (2023): 113–19. http://dx.doi.org/10.30864/eksplora.v12i2.819.

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Clustering adalah proses pengelompokkan tanpa pelatihan (unsupervised learning), salah satu algoritma yang dapat diterapkan untuk clustering adalah K-Means. Algoritma ini memiliki kinerja dengan konsep menghitung jarak terdekat dari sebuah cluster. Penelitian ini bertujuan untuk melakukan optimasi hasil clustering data abstrak skripsi dengan algoritma K-Means tersebut. Upaya yang dilakukan untuk optimalisasi hasil cluster adalah dengan model kombinasi algoritma Latent Semantic Analysis (LSA), Term Frequency – Inverse Document Frequency (TF-IDF) dan Hashing. Seperti penanganan data teks pada um

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19

Ni, Jingkai. "Research on clustering analysis of eye diagram point set of digital signal based on equivalent time sampling." Highlights in Science, Engineering and Technology 105 (June 30, 2024): 273–82. http://dx.doi.org/10.54097/e890q450.

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With the continuous development and improvement of communication technology, the research and application of high-frequency signals are becoming more and more important, especially in important fields such as electronic communication, aerospace and aviation. The analysis of high-frequency signals is the most basic and most important. High-frequency digital signals are mainly obtained by equivalent time sampling and sequential sampling. This paper first analyzes the basic principles of equivalent time sampling and real-time sampling, and compares the advantages and disadvantages of the two and

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20

Davis, Ryan L., Bonnie F. Van Ness, and Robert A. Van Ness. "Clustering of Trade Prices by High-Frequency and Non-High-Frequency Trading Firms." Financial Review 49, no. 2 (2014): 421–33. http://dx.doi.org/10.1111/fire.12042.

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21

Harris, Michael B., Richard J. A. Wilson, Konstantinon Vasilakos, Barbara E. Taylor, and John E. Remmers. "Central respiratory activity of the tadpole in vitro brain stem is modulated diversely by nitric oxide." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 283, no. 2 (2002): R417—R428. http://dx.doi.org/10.1152/ajpregu.00513.2001.

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Nitric oxide (NO) is a potent central neuromodulator of respiration, yet its scope and site of action are unclear. We used 7-nitroindazole (7-NI), a selective inhibitor of endogenous neuronal NO synthesis, to investigate the neurogenesis of respiration in larval bullfrog ( Rana catesbeiana) isolated brain stems. 7-NI treatment (0.0625–0.75 mM) increased the specific frequency of buccal ventilation (BV) events, indicating influence on BV central rhythm generators (CRGs). The drug reduced occurrence, altered burst shape, and disrupted clustering of lung ventilation (LV) events, without altering

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22

Ziskind, Avi J., Al A. Emondi, Andrei V. Kurgansky, Sergei P. Rebrik, and Kenneth D. Miller. "Neurons in cat V1 show significant clustering by degree of tuning." Journal of Neurophysiology 113, no. 7 (2015): 2555–81. http://dx.doi.org/10.1152/jn.00646.2014.

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Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial frequencies, and, for drifting gratings, directions. Orientation tuning width, spatial frequency tuning width, and direction selectivity index (DSI) all showed significant clustering: pairs of neurons rec

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23

Kirner, Thomas, and Otto E. Rössler. "Frequency Clustering in a Chain of Weakly Coupled Oscillators." Zeitschrift für Naturforschung A 52, no. 8-9 (1997): 578–80. http://dx.doi.org/10.1515/zna-1997-8-904.

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Abstract A numerical simulation of a chain of diffusively coupled nonlinear oscillators with a linear parameter gradient exhibits clusters of frequencies. The intention was to investigate the frequency-gradient in the stimulus conduction system of the heart. The phenomenon generalizes earlier findings on “frequency plateaus” described in the 1960's by Nicholas Diamant as a model of small-intestine transport. This “waxing and waining” phenomenon is a version of chaos. Thus, subtle chaos in the heart and waxing and waining type chaos in the intestine may be related.

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24

Basu, Bidroha, and V. V. Srinivas. "Regional Flood Frequency Analysis Using Entropy-Based Clustering Approach." Journal of Hydrologic Engineering 21, no. 8 (2016): 04016020. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001351.

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25

Sokolowski, Jakub, Jakub Obuchowski, Maciej Madziarz, Agnieszka Wylomanska, and Radosław Zimroz. "Features based on instantaneous frequency for seismic signals clustering." Journal of Vibroengineering 18, no. 3 (2016): 1654–67. http://dx.doi.org/10.21595/jve.2016.16823.

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26

Maharaj, Elizabeth Ann, and Pierpaolo D’Urso. "Fuzzy clustering of time series in the frequency domain." Information Sciences 181, no. 7 (2011): 1187–211. http://dx.doi.org/10.1016/j.ins.2010.11.031.

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27

JOHAN, H. "Clustering Environment Lights for an Efficient All-Frequency Relighting." IEICE Transactions on Information and Systems E89-D, no. 9 (2006): 2562–71. http://dx.doi.org/10.1093/ietisy/e89-d.9.2562.

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28

Soares, Victor Hugo Andrade, Ricardo J. G. B. Campello, Seyednaser Nourashrafeddin, Evangelos Milios, and Murilo Coelho Naldi. "Combining semantic and term frequency similarities for text clustering." Knowledge and Information Systems 61, no. 3 (2019): 1485–516. http://dx.doi.org/10.1007/s10115-018-1278-7.

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29

Holan, Scott H., and Nalini Ravishanker. "Time series clustering and classification via frequency domain methods." Wiley Interdisciplinary Reviews: Computational Statistics 10, no. 6 (2018): e1444. http://dx.doi.org/10.1002/wics.1444.

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30

Masich, Igor, Guzel Shkaberina, and Danila Masich. "Binarization of features based on frequency discretization for clustering tasks." ITM Web of Conferences 72 (2025): 04003. https://doi.org/10.1051/itmconf/20257204003.

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This paper explores the transformation of heterogeneous features, including continuous data, into binary form using frequency discretization. This method is particularly beneficial for clustering tasks, as binary features simplify the interpretation of results using logical expressions. In unsupervised learning, where class labels are unknown, we propose a binarization approach that converts continuous features into binary values based on their frequency distribution. Our experiments show that this technique not only preserves essential information but also improves clustering quality, as meas

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31

Akshata, Upadhye. "Discovering Hidden Themes by Enhancing Document Cluster Interpretability." European Journal of Advances in Engineering and Technology 6, no. 2 (2019): 66–73. https://doi.org/10.5281/zenodo.10889970.

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<strong>ABSTRACT</strong> In this era of digital transformation, as the digital information continues to grow it presents a formidable challenge in organizing and understanding documents. Most of the clustering algorithms efficiently group similar documents, yet they have limitations such as revealing the hidden themes that define each cluster that hinder effective comprehension. Motivated by this gap that lies between clustering and understanding the clustering, this research addresses this gap by leveraging Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) representa

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32

Surianto, Dewi Fatmarani, and Dewi Fatmawati Surianto. "Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction." Brilliance: Research of Artificial Intelligence 4, no. 2 (2025): 964–72. https://doi.org/10.47709/brilliance.v4i2.5547.

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Clustering is a fundamental technique in data analysis, particularly in unsupervised learning, to group data with similar characteristics. However, the effectiveness of the K-Means algorithm in text clustering heavily depends on proper feature extraction. This study proposes an enhanced feature extraction approach by integrating Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) to improve clustering performance on journal article datasets. The dataset consists of 427 journal article abstracts collected from Google Scholar. The preprocessing steps include

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33

Wiyono, Slamet, and Rais Rais. "The Use of Clustering Methods in Memory-Based Collaborative Filtering for Ranking-Based Recommendation Systems." Buletin Ilmiah Sarjana Teknik Elektro 5, no. 4 (2024): 599–605. https://doi.org/10.12928/biste.v5i4.9435.

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This research explores the application of clustering techniques and frequency normalization in collaborative filtering to enhance the performance of ranking-based recommendation systems. Collaborative filtering is a popular approach in recommendation systems that relies on user-item interaction data. In ranking-based recommendation systems, the goal is to provide users with a personalized list of items, sorted by their predicted relevance. In this study, we propose a novel approach that combines clustering and frequency normalization techniques. Clustering, in the context of data analysis, is

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34

C Reghuraj P, Sreejith. "Isolated Spoken Word Identification in Malayalam using Mel-frequency Cepstral Coefficients and K-means clustering." International Journal of Science and Research (IJSR) 1, no. 3 (2012): 163–67. http://dx.doi.org/10.21275/ijsr12120377.

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35

Wu, Jing, and Guang Xue Meng. "A New Clustering Algorithm and Relevant Theoretical Analysis for Ad-Hoc Networks." Applied Mechanics and Materials 556-562 (May 2014): 4001–4. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4001.

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In ad-hoc networks, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a new clustering algorithm (NCA) was proposed. Firstly, NCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that NCA can improve cluster stability and reduce clustering overheads.

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36

Hou, Guo Zhao, Jin Biao Wang, and Jing Wu. "MANET-Based Stable Clustering Algorithm and its Performance Analysis." Applied Mechanics and Materials 571-572 (June 2014): 100–104. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.100.

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In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.

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37

Kang, Yu, Erwei Liu, Kaichi Zou, Xiuyun Wang, and Huaqing Zhang. "Sparse Clustering Algorithm Based on Multi-Domain Dimensionality Reduction Autoencoder." Mathematics 12, no. 10 (2024): 1526. http://dx.doi.org/10.3390/math12101526.

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The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering results. In order to address these issues, we propose a sparse clustering algorithm based on a multi-domain dimensionality reduction model. This method achieves high-dimensional clustering by integrating the sparse reconstruction process and sparse L1 regularization into a deep autoencoder model. A

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38

Khan, Danish. "Modeling and Semantic Clustering in Large-scale Text Data: A Review of Machine Learning Techniques and Applications." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46510.

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Abstract With the exponential growth of textual data across diverse domains, the task of efficiently modelling and clustering large-scale text has emerged as a key challenge in natural language processing (NLP). Conventional text representation approaches, such as Term Frequency-Inverse Document Frequency (TF-IDF) and Bag-of-Words (BoW), often fall short in capturing semantic nuances. This limitation has encouraged the adoption of more advanced techniques, including word embeddings (e.g., Word2Vec, GloVe) and transformer-based models like BERT and GPT. Similarly, traditional clustering algorit

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Jiang, Xuchu, Xinyong Mao, Yingjie Chen, and Caihua Hao. "A new modal analysis method applied to changing machine tool using clustering." Advances in Mechanical Engineering 12, no. 10 (2020): 168781402096832. http://dx.doi.org/10.1177/1687814020968323.

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The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clu

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40

T. Elavarasi. "Spectral Clustering-Based Particle Swarm Optimization Algorithm for Document Clustering." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 134–46. https://doi.org/10.52783/jisem.v10i4s.487.

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The process of automatically grouping documents into clusters such that the documents in one cluster are very comparable to the documents in the remaining clusters have been known as document clustering. Due to its broad application in a number of fields, including search engines, web mining, and information retrieval, it has been the subject of much research. It involves clustering documents that are identical to one another and calculating how identical they are. It facilitates simple navigation by offering effective document representation as well as visualization. Hence, this research pape

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Hasib, Mohammad, Bagas Anwar Arif Nur, Huffaz Muhammad Abdurrofi Baith, et al. "Event classification of volcanic earthquakes based on K-Means clustering: Application on Anak Krakatau Volcano, Sunda Strait." IOP Conference Series: Earth and Environmental Science 1314, no. 1 (2024): 012045. http://dx.doi.org/10.1088/1755-1315/1314/1/012045.

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Abstract It is important to quickly recognize any physical changes in volcanology and accompanying phenomena at each stage of an eruption in terms of mitigating volcanic eruptions. Automatic classification of the type of volcanic earthquake is required, especially since the data recorded by seismic equipment is classified as big data. Analyzing big data manually will take a lot of time. Therefore, we use unsupervised machine learning such as K-means clustering to generate an automated system of classifying the volcanic events based on their waveform and spectrum characteristics. We examine the

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42

Vilfan, Andrej, and Thomas Duke. "Frequency Clustering in Spontaneous Otoacoustic Emissions from a Lizard's Ear." Biophysical Journal 95, no. 10 (2008): 4622–30. http://dx.doi.org/10.1529/biophysj.108.130286.

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43

Krishnaraj, Dr N., Dr P. Kumar, and Sri K. Bhagavan. "Conceptual Semantic Model for Web Document Clustering Using Term Frequency." EAI Endorsed Transactions on Energy Web 5, no. 20 (2018): 155744. http://dx.doi.org/10.4108/eai.12-9-2018.155744.

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Zaifoglu, H., B. Akintug, and A. M. Yanmaz. "Regional Frequency Analysis of Precipitation Using Time Series Clustering Approaches." Journal of Hydrologic Engineering 23, no. 6 (2018): 05018007. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001659.

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Morelli, L. G., H. A. Cerdeira, and D. H. Zanette. "Frequency clustering of coupled phase oscillators on small-world networks." European Physical Journal B 43, no. 2 (2005): 243–50. http://dx.doi.org/10.1140/epjb/e2005-00046-2.

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46

Basheer, Shakila, S. Mariyam Aysha Bivi, G. Kavitha, and P. V. Praveen Sundar. "Distribution of Frequency Words Using Hierarchal Clustering Method in R." Journal of Computational and Theoretical Nanoscience 16, no. 5 (2019): 2531–34. http://dx.doi.org/10.1166/jctn.2019.7927.

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47

Fang, Chonglun, Wei Jin, and Jinwen Ma. "-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics." Pattern Recognition Letters 34, no. 5 (2013): 580–86. http://dx.doi.org/10.1016/j.patrec.2012.11.004.

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48

Myrden, Andrew, and Tom Chau. "Feature clustering for robust frequency-domain classification of EEG activity." Journal of Neuroscience Methods 262 (March 2016): 77–84. http://dx.doi.org/10.1016/j.jneumeth.2016.01.014.

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49

Hashemi, Hosein. "Fuzzy Clustering of Seismic Sequences: Segmentation of Time-Frequency Representations." IEEE Signal Processing Magazine 29, no. 3 (2012): 82–87. http://dx.doi.org/10.1109/msp.2012.2185897.

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M. Lopes, Antonio, and Jose Tenreiro Machado. "Tidal Analysis Using Time–Frequency Signal Processing and Information Clustering." Entropy 19, no. 8 (2017): 390. http://dx.doi.org/10.3390/e19080390.

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