Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Robust semi-supervised clustering with polyhedral and circular uncertainty
Date
2017-11-22
Author
DİNLER, DERYA
Tural, Mustafa Kemal
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
243
views
0
downloads
Cite This
We consider a semi-supervised clustering problem where the locations of the data objects are subject to uncertainty. Each uncertainty set is assumed to be either a closed convex bounded polyhedron or a closed disk. The final clustering is expected to be in accordance with a given number of instance level constraints. The objective function considered minimizes the total of the sum of the violation costs of the unsatisfied instance level constraints and a weighted sum of squared maximum Euclidean distances between the locations of the data objects and the centroids of the clusters they are assigned to. Given a cluster, we first consider the problem of computing its centroid, namely the centroid computation problem under uncertainty (CCPU).. We show that the CCPU can be modeled as a second order cone programing problem and hence can be efficiently solved to optimality. As the CCPU is one of the key ingredients of the several algorithms considered in this paper, a subgradient algorithm is also adopted for its faster solution. We then propose a mixed-integer second order cone programing formulation for the considered clustering problem which is only able to solve small-size instances to optimality. For larger instances, approaches from the semi-supervised clustering literature are modified and compared in terms of computational time and quality.
Subject Keywords
Cognitive Neuroscience
,
Artificial Intelligence
,
Computer Science Applications
URI
https://hdl.handle.net/11511/41584
Journal
NEUROCOMPUTING
DOI
https://doi.org/10.1016/j.neucom.2017.04.073
Collections
Department of Industrial Engineering, Article
Suggestions
OpenMETU
Core
Robust semi supervised clustering with polyhedral and circular uncertainty
Dinler, Derya; Tural, Mustafa Kemal (null; 2016-07-03)
We consider a semi-supervised clustering problem where the locations of the data objects are subject to uncertainty. Each uncertainty set is assumed to be either a closed convex bounded polyhedron or a closed disk. The final clustering is expected to be in accordance with a given number of instance level constraints. The objective function considered minimizes the total of the sum of the violation costs of the unsatisfied instance level constraints and a weighted sum of squared maximum Euclidean distances b...
ANFIS_unfolded_in_time for multivariate time series forecasting
Sisman-Yilmaz, Na; Alpaslan, Ferda Nur; Jain, L (Elsevier BV, 2004-10-01)
This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules. It is a modification of ANFIS neuro-fuzzy model. The rule base of ANFIS_unfolded_in_time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, back-propagation learning algorithm is used. The system takes the multivariate data and the number o...
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Buyuksahin, Umit Cavus; Ertekin Bolelli, Şeyda (Elsevier BV, 2019-10-07)
Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist that use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Au...
Generation of cyclic/toroidal chaos by Hopfield neural networks
Akhmet, Marat (Elsevier BV, 2014-12-05)
We discuss the appearance of cyclic and toroidal chaos in Hopfield neural networks. The theoretical results may strongly relate to investigations of brain activities performed by neurobiologists. As new phenomena, extension of chaos by entrainment of several limit cycles as well as the attraction of cyclic chaos by an equilibrium are discussed. Appropriate simulations that support the theoretical results are depicted. Stabilization of tori in a chaotic attractor is realized not only for neural networks, but...
Attraction of Li-Yorke chaos by retarded SICNNs
Akhmet, Marat (Elsevier BV, 2015-01-05)
In the present study, dynamics of retarded shunting inhibitory cellular neural networks (SICNNs) is investigated with Li-Yorke chaotic external inputs and outputs. Within the scope of our results, we prove the presence of generalized synchronization in coupled retarded SICNNs, and confirm it by means of the auxiliary system approach. We have obtained more than just synchronization, as it is proved that the Li-yorke chaos is extended with its ingredients, proximality and frequent separation, which have not b...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
D. DİNLER and M. K. Tural, “Robust semi-supervised clustering with polyhedral and circular uncertainty,”
NEUROCOMPUTING
, pp. 4–27, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41584.