Your selections:
- Bagirov, Adil, Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
Introduction to Nonsmooth Optimization : Theory, practice and software
- Bagirov, Adil, Karmitsa, Napsu, Makela, Marko
Lagrange-type functions in constrained optimization
- Rubinov, Alex, Yang, Xiao, Bagirov, Adil, Gasimov, Rafail
Limited Memory Bundle Method for Clusterwise Linear Regression
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona, Joki, Kaisa
Limited memory discrete gradient bundle method for nonsmooth derivative-free optimization
- Karmitsa, Napsu, Bagirov, Adil
Local optimization method with global multidimensional search
- Bagirov, Adil, Rubinov, Alex, Zhang, Jiapu
Machine learning algorithms for analysis of DNA data sets
- Yearwood, John, Bagirov, Adil, Kelarev, Andrei
Malware variant identification using incremental clustering
- Black, Paul, Gondal, Iqbal, Bagirov, Adil, Moniruzzaman, Md
Max-min separability
Methods and applications of clusterwise linear regression : a survey and comparison
- Long, Qiang, Bagirov, Adil, Taheri, Sona, Sultanova, Nargiz, Wu, Xue
- Barton, Andrew, Mala-Jetmarova, Helena, Nuamat, Alia Mari Al, Bagirov, Adil, Sultanova, Nargiz, Ahmed, Shams
Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations
- Gaudioso, Manlio, Giallombardo, Giovanni, Miglionico, Giovanna, Bagirov, Adil
Missing value imputation via clusterwise linear regression
- Karmitsa, Napsu, Taheri, Sona, Bagirov, Adil, Makinen, Pauliina
In this paper a new method of preprocessing incomplete data is introduced. The method is based on clusterwise linear regression and it combines two well-known approaches for missing value imputation: linear regression and clustering. The idea is to approximate missing values using only those data points that are somewhat similar to the incomplete data point. A similar idea is used also in clustering based imputation methods. Nevertheless, here the linear regression approach is used within each cluster to accurately predict the missing values, and this is done simultaneously to clustering. The proposed method is tested using some synthetic and real-world data sets and compared with other algorithms for missing value imputations. Numerical results demonstrate that the proposed method produces the most accurate imputations in MCAR and MAR data sets with a clear structure and the percentages of missing data no more than 25%
Modified global k-means algorithm for clustering in gene expression data sets
- Bagirov, Adil, Mardaneh, Karim
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Modified self-organising maps with a new topology and initialisation algorithm
- Mohebi, Ehsan, Bagirov, Adil
- Dey, Sayani, Barton, Andrew, Bagirov, Adil, Kandra, Harpreet, Wilson, Kym
Multi-source cyber-attacks detection using machine learning
- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung
New algorithms for multi-class cancer diagnosis using tumor gene expression signatures
- Bagirov, Adil, Ferguson, Brent, Ivkovic, Sasha, Saunders, Gary, Yearwood, John
New diagonal bundle method for clustering problems in large data sets
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona
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