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A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

Qiong Yuan, Guangming Dai
2016 Journal of Software  
According to the traditional GA and EDA weakness, on the basis of MMEA, the orthogonal design initialization, convergence criterion and K-means clustering analysis method were introduced in this paper  ...  The practice results showed that the OMEA had been greatly improved on both convergence and diversity of the solutions, reaching a good balance on diversity and convergence.  ...  The results showed that the new algorithm OMEA both in terms of the diversity and the convergence of solution had been improved.  ... 
doi:10.17706/jsw.11.8.733-744 fatcat:tmxzwxhik5d37nliv22e66km3y

An Incremental K-means algorithm

D T Pham, S S Dimov, C D Nguyen
2004 Proceedings of the Institution of mechanical engineers. Part C, journal of mechanical engineering science  
One of the most popular data clustering methods is K-means clustering because of its simplicity and computational efficiency.  ...  The scheme, which is an incremental version of the K-means algorithm, involves adding cluster centres one by one as clusters are being formed.  ...  The MS was received on 20 August 2003 and was accepted after revision for publication on 26 March 2004.  ... 
doi:10.1243/0954406041319509 fatcat:k54rrz5iefarpb6icvwt62gz34

Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository

Saeed Hassanpour, Curtis P. Langlotz
2015 Journal of digital imaging  
The free text format and the subtlety and variations of natural language hinder the extraction of reusable information from radiology reports for decision support, quality improvement, and biomedical research  ...  In this approach, radiology reports are modeled in a vector space and compared to each other through a cosine similarity measure.  ...  Acknowledgments The authors would like to thank Chuck Kahn, Kevin McEnery, and Brad Erickson for their work on compiling RadCore database and Daniel Rubin for his contribution to RadCore and providing  ... 
doi:10.1007/s10278-015-9823-3 pmid:26353748 pmcid:PMC4722022 fatcat:yh4tag7kyjfwhfjoucdduullxa

Category Variable Selection Method for Efficient Clustering

Jun Heo, Chae Yun Kim, Yong-Gyu Jung
2013 International journal of advanced smart convergence  
In this study, the drugs to the patient according to the component analysis and predictions for future research techniques, k-means clustering and k-NN (Nearest Neighbor) Comparative studies through experiments  ...  The nation's health care industry through new support expansion and improve the quality of life for the research and development will be needed.  ...  In fact , this can be achieved through the study of k-means clustering is a clustering analysis to proceed as a result of a small amount of data , and you can proceed to set the initial value data , but  ... 
doi:10.7236/ijasc2013.2.2.9 fatcat:noqhipfq4zdcnjl2o5px2xl2si

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Aidin Delgoshaei, Armin Delgoshaei, Ahad Ali
2019 International Journal of Industrial Engineering Computations  
This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems.  ...  Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided.  ...  Acknowledgments The Authors would like to thank Professor Mohd Khairol Ariffin (University of Putra Malaysia) for his positive comments.  ... 
doi:10.5267/j.ijiec.2018.8.002 fatcat:72pk7ji4jzdixgvkepgz5ujn7i

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Laith Abualigah, Amir H. Gandomi, Mohamed Abd Elaziz, Husam Al Hamad, Mahmoud Omari, Mohammad Alshinwan, Ahmad M. Khasawneh
2021 Electronics  
This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures.  ...  The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.  ...  This paper proposed a new method called GSA-KHM combined between GSA and K-harmonic means to improve dependency on the initialization [75] .  ... 
doi:10.3390/electronics10020101 fatcat:fb3sopje4fegphs5b6g673ipqa

A variant of genetic algorithm for non-homogeneous population

Najmeh Alibabaie, Mohammad Ghasemzadeh, Christoph Meinel, N. Bardis, J. Quartieri, C. Guarnaccia, N. Doukas
2017 ITM Web of Conferences  
The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers.  ...  Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes.  ...  The k-means algorithm is the simplest and the most popular clustering method [2] . The initial cluster centers usually have a high efficacy on performance of the k-means algorithm.  ... 
doi:10.1051/itmconf/20170902001 fatcat:lwyvw62jezenbgehgkdb26qovq

Improving fuzzy clustering of biological data by metric learning with side information

Michele Ceccarelli, Antonio Maratea
2008 International Journal of Approximate Reasoning  
In this paper we use a Metric Learning approach as a way to improve the classical fuzzy c-means clustering through a two steps procedure: first a series of metrics (one for each cluster) that satisfy a  ...  When using a clustering algorithm, the auxiliary information has the form of side information, that is a list of co-clustered points.  ...  We have shown the efficacy and the limitations of using side information in unsupervised techniques.  ... 
doi:10.1016/j.ijar.2007.03.008 fatcat:6pe5mazajjephernorkmafplay

A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines

Yu Zhang, Jun Chen, Chris Bingham, Mahdi Mahfouf
2014 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system.  ...  The paper presents a new system identification methodology for industrial systems.  ...  ACKNOWLEDGMENT The authors would like to thank Siemens Industrial Turbomachinery, Lincoln, U.K., for providing research support and access to real-time data to support the research outcomes.  ... 
doi:10.1109/fuzz-ieee.2014.6891815 dblp:conf/fuzzIEEE/ZhangCBM14 fatcat:cpkxkujo5rak7njuozffey3lfy

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

Khawla Tadist, Fatiha Mrabti, Nikola S. Nikolov, Azeddine Zahi, Said Najah
2021 Journal of Big Data  
Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale.  ...  The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods.  ...  Acknowledgements The authors thank the anonymous reviewers for their helpful suggestions and comments. Authors' contributions All mentioned authors contribute in the elaboration of the paper.  ... 
doi:10.1186/s40537-021-00409-x fatcat:wjbh7basxvbjtp5bxvbhluaggi

A REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION METHODS AND APPLICATIONS

Mehdi Neshat, Ali Adeli, Ghodrat Sepidnam, Mehdi Sargolzaei, Adel Najaran Toosi
2012 International Journal on Smart Sensing and Intelligent Systems  
The Swarm Intelligence is a new and modern method employed in optimization problems.  ...  This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review  ...  They introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a K-means clustering algorithm is optimized by AFSA in the learning process of RBF.  ... 
doi:10.21307/ijssis-2017-474 fatcat:z3mlrntshjfafndeh7s5ta6yum

An Efficient Approach to Solve the Large-Scale Semidefinite Programming Problems

Yongbin Zheng, Yuzhuang Yan, Sheng Liu, Xinsheng Huang, Wanying Xu
2012 Mathematical Problems in Engineering  
The numerical experiments show that our approach is efficient and scales very well with the problem dimension. Furthermore, the proposed algorithm is applied for a clustering problem.  ...  The experimental results on real datasets imply that the proposed approach outperforms the traditional interior-point SDP solvers in terms of efficiency and scalability.  ...  The accuracy of clustering each dataset of our algorithm is compared with K-means, spectral clustering, and CVX here CVX means the SDP problem in 3.2 is solved using the CVX toolbox .  ... 
doi:10.1155/2012/764760 fatcat:h23py57dx5drnn53ci3ji3of5a

Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering [article]

Michael Weylandt and T. Mitchell Roddenberry and Genevera I. Allen
2021 arXiv   pre-print
In many applications where we observe noisy signals, it is common practice to first denoise the data, perhaps using wavelet denoising, and then to apply a clustering algorithm.  ...  Clustering is a ubiquitous problem in data science and signal processing.  ...  We also demonstrate the efficacy of sparse convex wavelet clustering through synthetic and real datasets, illustrating desirable properties compared to existing and commonly employed methods.  ... 
arXiv:2012.04762v2 fatcat:rfrgu64penh4hjrz72tg47e5si

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Yugal Kumar, Neeraj Dahiya, Sanjay Malik, Savita Khatri
2019 Informatica (Ljubljana, Tiskana izd.)  
Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners.  ...  In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies.  ...  ., have presented a new version of TLBO algorithm, called mTLBO for improving the convergence rate [26] .  ... 
doi:10.31449/inf.v43i1.1636 fatcat:vpznf3aosbd7hldwsag7rmdcta

SimpleMKKM: Simple Multiple Kernel K-means [article]

Xinwang Liu, En Zhu, Jiyuan Liu, Timothy Hospedales, Yang Wang, Meng Wang
2020 arXiv   pre-print
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).  ...  To optimize it, we re-formulate the problem as a smooth minimization one, which can be solved efficiently using a reduced gradient descent algorithm.  ...  We then briefly discuss the convergence of SimpleMKKM. Note that Eq. (6) is a traditional kernel k-means which has a global optimum.  ... 
arXiv:2005.04975v2 fatcat:eze436xyyrecdmmfswf63zemgq
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