Prof. Zeynel Cebeci
Çukurova University
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
22Years (2005-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications14 October 2015 in Journal of Agricultural Informatics
Journal Article
Zeynel CebeciFYFigen Yildiz
Figen Yildiz
In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.
1 January 2006 in Interdisciplinary Journal of e-Skills and Lifelong Learning
Journal Article
Zeynel CebeciMTMehmet Tekdal
Mehmet Tekdal
An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.
31 December 2019 in Journal of Agricultural Informatics
Journal Article
Zeynel CebeciPartitioning clustering has been one of the key components of data analytics to discover meaningful patterns in agricultural big data, driven by the increasing use of IoT-based technologies in smart farming. In partitioning clustering, the quality of clustering or performances of clustering algorithms are mostly evaluated by using the internal validity indices. In this study, the effectiveness of some widely used internal fuzzy indices are compared using the basic Fuzzy C-Means clustering algorithm. It is especially aimed to investigate changes in the effectiveness of validity indices when fuzzy data points are at different distances from the cluster centers. According to the results obtained on the simulated two-dimensional datasets, Fuzzy Silhouette, Fuzzy Hypervolume and Kwon are the most successful indices in validation of fuzzy clustering results.
30 April 2020 in Sakarya University Journal of Computer and Information Sciences
Journal Article
Zeynel CebeciIn exploratory data analysis and machine learning, partitioning clustering is a frequently used unsupervised learning technique for finding the meaningful patterns in numeric datasets. Clustering aims to identify and classify the objects or the cases in datasets in practice. The clustering quality or the performance of a clustering algorithm is generally evaluated by using the internal validity indices. In this study, an R package named 'fcvalid' is introduced for validation of fuzzy and possibilistic clustering results. The package implements a broad collection of the internal indices which have been proposed to validate the results of fuzzy clustering algorithms. Additionally, the options to compute the generalized and extended versions of the fuzzy internal indices for validation of the possibilistic clustering are also included in the package.
1 March 2019 in Applied Clay Science
Journal Article
AÇAhmet Çelik
Ahmet Çelik
SKSelahattin Kadir
Selahattin Kadir
SKSelim Kapur
Selim Kapur
KZKemal Zorlu
Kemal ZorluEAErhan Akça
Erhan Akça
Zeynel Cebeci +1
1 September 2018
Conference Paper
Zeynel CebeciIn fuzzy clustering, initialization of cluster centers and membership degree matrices is one of the mandatory tasks for starting the fuzzy partitioning algorithms. Quality initialization of these matrices provides fast convergence to the final cluster centers. This reduces the required number of iterations and consequently results with low computational costs. In this paper, a novel algorithm is proposed to generate initial membership degree matrix for starting fuzzy partitioning clustering algorithms such as Fuzzy C-means, Possibilistic Fuzzy C-means and other variants of these algorithms. The proposed initialization algorithm is based on the selection of the feature with the highest variability among the others in a multidimensional numeric dataset. The experimental results show that the proposed algorithm performs well in reducing the number of iterations needed to converge to the final cluster centers for the examined synthetic and real datasets.
About Me
He was graduated from Faculty of Agriculture (FAg) of the Cukurova University in Adana-Turkey (CU) in 1983. He completed his M.Sc. and Ph.D. education on best linear unbiased prediction methodologies in the Biometry and Genetics Unit in the Institute of Basic and Applied Sciences (IBAS) of CU in 1985 and 1990, respectively. During his Ph.D. education, he also participated to the various research activities and the courses at the Dept. of Animal genetics (Fachgebiet Haustiergenetik) at the Hohenheim University in Stutgart - Germany.
He worked as a senior software developer and manager in private computing businesses, and research asistant and assistant professor at CU from 1985 to 1994. He owned the associate professorship degree in 1993, and was assigned as a full professor in 1999 at CU. At the present time he works an academic staff member in the Biometry and Genetics Unit of FAg and IBAS. He teaches the courses on information technology, data science and statistics in the undergraduate and graduate degree programmes at CU. His research interests include mainly parametric and non-parametric statistical methods, data sciences, bioinformatics sofware development, food traceability systems, blockchains, learning technologies, and development of the multilingual web systems.
He is a member of Turkish Statistical Association, Turkish Informatics Society, Foundation of Education, Research and Development of FAg, and the Society of Information Technologies in Agriculture, Food,