Decision making system algorithm on menopause data set

Bacak, Hikmet Özge
Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process relies upon a “similarity measure” between clusters defined in the thesis. During the merging process, the cluster center coordinates do not change but the data members in these clusters are merged in a new cluster. As the output of this method, therefore, one obtains clusters which include many cluster centers. In the final part of this study, an application of the clustering algorithms – including the multiple centered clustering method – a decision making system is constructed using a special data on menopause treatment. The decisions are based on the clusterings created by the algorithms already discussed in the previous chapters of the thesis. A verification of the decision making system / v decision aid system is done by a team of experts from the Department of Department of Obstetrics and Gynecology of Hacettepe University under the guidance of Prof. Sinan Beksaç.


Multiple criteria project selection problems
Çağlar, Musa; Karasakal, Esra; Department of Industrial Engineering (2009)
In this study, we propose two biobjective mathematical models based on PROMETHEE V method for project selection problems. We develop an interactive approach (ib-PROMETHEE V) including data mining techniques to solve the first proposed mathematical model. For the second model, we propose NSGA-II with constraint handling method. We also develop a Preference Based Interactive Multiobjective Genetic Algorithm (IMGA) to solve the second proposed mathematical model. We test the performance of NSGA-II with constra...
Identification of functionally orthologous protein groups in different species based on protein network alignment
Yaveroğlu, Ömer Nebil; Can, Tolga; Department of Computer Engineering (2010)
In this study, an algorithm named ClustOrth is proposed for determining and matching functionally orthologous protein clusters in different species. The algorithm requires protein interaction networks of the organisms to be compared and GO terms of the proteins in these interaction networks as prior information. After determining the functionally related protein groups using the Repeated Random Walks algorithm, the method maps the identified protein groups according to the similarity metric defined. In orde...
A novel neural network based approach for direction of arrival estimation
Çaylar, Selçuk; Dural Ünver, Mevlüde Gülbin; Department of Electrical and Electronics Engineering (2007)
In this study, a neural network(NN) based algorithm is proposed for real time multiple source tracking problem based on a previously reported work. The proposed algorithm namely modified neural network based multiple source tracking algorithm (MN-MUST) performs direction of arrival(DoA) estimation in three stages which are the detection, filtering and DoA estimation stages. The main contributions of this proposed system are: reducing the input size for the uncorrelated source case (reducing the training tim...
Design of attitude estimation algorithms for inertial sensors only measurement scenarios
Candan, Batu; Söken, Halil Ersin; Department of Aerospace Engineering (2022-3-24)
This thesis proposes four novel robust Kalman filter algorithms for attitude estimation using only the measurements of an inertial measurement unit. Efficiency and optimality of the Kalman filter based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the inertial measurement unit-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain ro...
An fMRI segmentation method under markov random fields for brain decoding
Aksan, Emre; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015)
In this study, a specially tailored segmentation method for partitioning the fMRI data into a set of "homogenous" regions with respect to a predefined cost function is proposed. The proposed method, referred as f-MRF, employs univariate and multivariate fMRI data analysis techniques under Markov Random Fields to estimate the segments by resolving a mixture density. The univariate approach helps identifying activation pattern of a voxel independently from other voxels. In order to capture local interactions ...
Citation Formats
H. Ö. Bacak, “Decision making system algorithm on menopause data set,” M.S. - Master of Science, Middle East Technical University, 2010.