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A novel optimization approach towards improving separability of clusters
- Bagirov, Adil, Hoseini-Monjezi, Najmeh, Taheri, Sona
Bundle enrichment method for nonsmooth difference of convex programming problems
- Gaudioso, Manilo, Taheri, Sona, Bagirov, Adil, Karmitsa, Napsu
Methods and applications of clusterwise linear regression : a survey and comparison
- Long, Qiang, Bagirov, Adil, Taheri, Sona, Sultanova, Nargiz, Wu, Xue
Nonsmooth optimization-based hyperparameter-free neural networks for large-scale regression
- Karmitsa, Napsu, Taheri, Sona, Joki, Kaisa, Paasivirta, Pauliina, Defterdarovic, J., Bagirov, Adil, Mäkelä, Marko
Nonsmooth optimization-based model and algorithm for semisupervised clustering
- Bagirov, Adil, Taheri, Sona, Bai, Fusheng, Zheng, Fangying
Limited Memory Bundle Method for Clusterwise Linear Regression
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona, Joki, Kaisa
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%
Robust piecewise linear L 1-regression via nonsmooth DC optimization
- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu, Sultanova, Nargiz, Asadi, Soodabeh
Aggregate subgradient method for nonsmooth DC optimization
- Bagirov, Adil, Taheri, Sona, Joki, Kaisa, Karmitsa, Napsu, Mäkelä, Marko
An augmented subgradient method for minimizing nonsmooth DC functions
- Bagirov, Adil, Hoseini Monjezi, Najmeh, Taheri, Sona
Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Bagirov, Adil, Taheri, Sona, Cimen, Emre
Clusterwise support vector linear regression
- Joki, Kaisa, Bagirov, Adil, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu
- Bagirov, Adil, Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
- Bagirov, Adil, Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko, Taheri, Sona
A difference of convex optimization algorithm for piecewise linear regression
- Bagirov, Adil, Taheri, Sona, Asadi, Soodabeh
- Bagirov, Adil, Taheri, Sona, Bai, Fusheng, Wu, Zhiyou
Multi-source cyber-attacks detection using machine learning
- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung
Clustering in large data sets with the limited memory bundle method
- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona
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