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Alsameen MH, Gong Z, Qian W, Kiely M, Triebswetter C, Bergeron CM, Cortina LE, Faulkner ME, Laporte JP, Bouhrara M. C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter. Front Neurol 2023; 14:1205426. [PMID: 37602266 PMCID: PMC10435293 DOI: 10.3389/fneur.2023.1205426] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/14/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI. Results ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM. Conclusion C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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Alomoush W, Khashan OA, Alrosan A, Damseh R, Attar HH, Alshinwan M, Abd-alrazaq AA. MRI brain segmentation based on improved global best-guided artificial bee colony.. [DOI: 10.21203/rs.3.rs-3097202/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Abstract
Brain Magnetic Resonance Imaging (MRI) plays a critical role in medical research and clinical applications, ranging from quantifying tissue volume, facilitating surgical simulations, assisting in treatment planning, enabling brain mapping, aiding in disease diagnosis, and evaluating therapeutic efficacy. This study introduces a novel method for MRI brain segmentation, which harnesses the power of a hybrid approach combining Artificial Bee Colony (ABC) algorithm with Fuzzy C-Means (FCM) clustering. Our approach leverages the exploration capability of the ABC algorithm, with an improved global best guidance (IABC), to optimally initialize the cluster centroid values of the FCM, thus enhancing the segmentation outputs. Comparative evaluation of the proposed method, denoted as IABC-FCM, conducted on a diverse set of MRI brain images, reveals its superior performance. The results indicate the potential of this hybrid approach as a robust tool for improved MRI brain segmentation tasks.
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Alomoush W, Khashan OA, Alrosan A, Houssein EH, Attar H, Alweshah M, Alhosban F. Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation. SENSORS (BASEL, SWITZERLAND) 2022; 22:8956. [PMID: 36433552 PMCID: PMC9694623 DOI: 10.3390/s22228956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.
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Affiliation(s)
- Waleed Alomoush
- School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates
| | - Osama A. Khashan
- Research and Innovation Centers, Rabdan Academy, Abu Dhabi P.O. Box 114646, United Arab Emirates
| | - Ayat Alrosan
- School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates
| | - Essam H. Houssein
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Hani Attar
- Department of Energy Engineering, Zarqa University, Zarqa 13132, Jordan
| | - Mohammed Alweshah
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan
| | - Fuad Alhosban
- CIS Department, Faculty of Computer Information Systems, Higher Colleges of Technology, Dubai P.O. Box 16062, United Arab Emirates
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Incremental Fuzzy Clustering Based on Feature Reduction. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2022. [DOI: 10.1155/2022/8566253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy.
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Murfi H. A scalable eigenspace-based fuzzy c-means for topic detection. DATA TECHNOLOGIES AND APPLICATIONS 2021. [DOI: 10.1108/dta-11-2020-0262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.
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Mittal N, Tayal S. Advance computer analysis of magnetic resonance imaging (MRI) for early brain tumor detection. Int J Neurosci 2020; 131:555-570. [PMID: 32241208 DOI: 10.1080/00207454.2020.1750390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
PURPOSE The brain tumor grows inside the skull and interposes with regular brain functioning. The tumor growth may possibly result in cancer at a later stage. The early detection of brain tumor is crucial for successful treatment of fatal disease. The tumor presence is normally detected by Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) images. The MRI/CT images are highly complex and involve huge data. This requires highly tedious and time-consuming process for detection of small tumors for the neurologists. Thus, there is a need to develop an effective and less time-consuming imaging technique for early detection of brain tumors. MATERIALS AND METHODS This paper mainly focuses on early detecting and localizing the brain tumor region using segmentation of patient's MRI images. The Matlab software experiments are performed on a set of fifteen tumorous MRI images. In the proposed work, four image segmentation modalities namely watershed transform, k-means clustering, thresholding and Fuzzy C Means Clustering techniques with median filtering have been implemented. RESULTS The results are verified by quantitative comparison of results in terms of image quality evaluation parameters-Entropy, standard deviation and Naturalness Image Quality Evaluator. A remarkable rise in the entropy and standard deviation values has been noticed. CONCLUSIONS The watershed transform segmentation with median filtering yields the best quality brain tumor images. The noteworthy improvement in visibility of the MRI images may highly increase the possibilities of early detection and successful treatment of brain tumor disease and thereby assists the clinicians to decide the precise therapies.
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Affiliation(s)
- Neetu Mittal
- Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, India
| | - Satyam Tayal
- Thapar Institute of Engineering and Technology, Patiala, India
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Zhao L, Chen Z, Yang Y, Zou L, Wang ZJ. ICFS Clustering With Multiple Representatives for Large Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:728-738. [PMID: 30047910 DOI: 10.1109/tnnls.2018.2851979] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
With the prevailing development of Cyber-physical-social systems and Internet of Things, large-scale data have been collected consistently. Mining large data effectively and efficiently becomes increasingly important to promote the development and improve the service quality of these applications. Clustering, a popular data mining technique, aims to identify underlying patterns hidden in the data. Most clustering methods assume the static data, thus they are unfavorable for analyzing large, unbalanced dynamic data. In this paper, to address this concern, we focus on incremental clustering by extending the novel [clustering by fast search (CFS) and find of density peaks] method to incrementally handle large-scale dynamic data. Specifically, we first discuss two challenges, i.e., assignment of new arriving objects and dynamic adjustment of clusters, in incremental CFS (ICFS) clustering. We then propose two ICFS clustering algorithms, ICFS with multiple representatives (ICFSMR) and the enhanced ICFSMR (E_ICFSMR) to tackle the two challenges. In ICFSMR, we explore the convex hull theory to modify the representatives identified for each cluster. E_ICFSMR improves the generality and effectiveness of ICFSMR by exploring one-time cluster adjustment strategy after integration of each data chunk. We evaluate the proposed methods with extensive experiments on four benchmark data sets, as well as the air quality and traffic monitoring time series, with comparisons to CFS and other three state-of-the-art incremental clustering methods. Experimental results demonstrate that the proposed methods outperform the compared methods in terms of both effectiveness and efficiency.
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Di Martino F, Sessa S. Extended Gustafson–Kessel granular hotspot detection. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0128-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu Y, Chen J, Wu S, Liu Z, Chao H. Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance. PLoS One 2018; 13:e0197499. [PMID: 29795600 PMCID: PMC5967819 DOI: 10.1371/journal.pone.0197499] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 05/03/2018] [Indexed: 11/18/2022] Open
Abstract
Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.
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Affiliation(s)
- Yongli Liu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
- * E-mail:
| | - Jingli Chen
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
| | - Shuai Wu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
| | - Zhizhong Liu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
| | - Hao Chao
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China
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Martino FD, Sessa S. Extended Fuzzy C-Means hotspot detection method for large and very large event datasets. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.02.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Kumar D, Bezdek JC, Palaniswami M, Rajasegarar S, Leckie C, Havens TC. A Hybrid Approach to Clustering in Big Data. IEEE TRANSACTIONS ON CYBERNETICS 2016; 46:2372-2385. [PMID: 26441434 DOI: 10.1109/tcyb.2015.2477416] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Clustering of big data has received much attention recently. In this paper, we present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the well known, classical batch k -means model. Specifically, we use k -means, single pass k -means, online k -means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data. We have performed experiments to show that k -means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slower than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97% of the ground truth labels in the real world KDD-99 cup data (4 292 637 samples in 41 dimensions) in 76 s.
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Li Y, Yang G, He H, Jiao L, Shang R. A study of large-scale data clustering based on fuzzy clustering. Soft comput 2016. [DOI: 10.1007/s00500-015-1698-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu Y, Wan X. Information bottleneck based incremental fuzzy clustering for large biomedical data. J Biomed Inform 2016; 62:48-58. [PMID: 27260783 DOI: 10.1016/j.jbi.2016.05.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 04/24/2016] [Accepted: 05/30/2016] [Indexed: 10/21/2022]
Abstract
Incremental fuzzy clustering combines advantages of fuzzy clustering and incremental clustering, and therefore is important in classifying large biomedical literature. Conventional algorithms, suffering from data sparsity and high-dimensionality, often fail to produce reasonable results and may even assign all the objects to a single cluster. In this paper, we propose two incremental algorithms based on information bottleneck, Single-Pass fuzzy c-means (spFCM-IB) and Online fuzzy c-means (oFCM-IB). These two algorithms modify conventional algorithms by considering different weights for each centroid and object and scoring mutual information loss to measure the distance between centroids and objects. spFCM-IB and oFCM-IB are used to group a collection of biomedical text abstracts from Medline database. Experimental results show that clustering performances of our approaches are better than such prominent counterparts as spFCM, spHFCM, oFCM and oHFCM, in terms of accuracy.
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Affiliation(s)
- Yongli Liu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China.
| | - Xing Wan
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
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Li N, Wu X, Xu D, Guo H, Feng W. Spatio-temporal context analysis within video volumes for anomalous-event detection and localization. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.12.064] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Alsmadi MK. MRI Brain Segmentation Using a Hybrid Artificial Bee Colony Algorithm with Fuzzy-C Mean Algorithm. ACTA ACUST UNITED AC 2014. [DOI: 10.3923/jas.2015.100.109] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Parker JK, Hall LO. Accelerating Fuzzy-C Means Using an Estimated Subsample Size. IEEE TRANSACTIONS ON FUZZY SYSTEMS : A PUBLICATION OF THE IEEE NEURAL NETWORKS COUNCIL 2014; 22:1229-1244. [PMID: 26617455 PMCID: PMC4662382 DOI: 10.1109/tfuzz.2013.2286993] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Many algorithms designed to accelerate the Fuzzy c-Means (FCM) clustering algorithm randomly sample the data. Typically, no statistical method is used to estimate the subsample size, despite the impact subsample sizes have on speed and quality. This paper introduces two new accelerated algorithms, GOFCM and MSERFCM, that use a statistical method to estimate the subsample size. GOFCM, a variant of SPFCM, also leverages progressive sampling. MSERFCM, a variant of rseFCM, gains a speedup from improved initialization. A general, novel stopping criterion for accelerated clustering is introduced. The new algorithms are compared to FCM and four accelerated variants of FCM. GOFCM's speedup was 4-47 times that of FCM and faster than SPFCM on each of the six datasets used in experiments. For five of the datasets, partitions were within 1% of those of FCM. MSERFCM's speedup was 5-26 times that of FCM and produced partitions within 3% of those of FCM on all datasets. A unique dataset, consisting of plankton images, exposed the strengths and weaknesses of many of the algorithms tested. It is shown that the new stopping criterion is effective in speeding up algorithms such as SPFCM and the final partitions are very close to those of FCM.
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Affiliation(s)
- Jonathon K Parker
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
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Exploring Big Data with Scalable Soft Clustering. SYNERGIES OF SOFT COMPUTING AND STATISTICS FOR INTELLIGENT DATA ANALYSIS 2013. [DOI: 10.1007/978-3-642-33042-1_2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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