Person: İNKAYA, TÜLİN
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İNKAYA
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TÜLİN
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Publication Extracting the boundaries of clusters: A post-clustering tool for spatial datasets(World Scientific Publ Co Pte Ltd, 2020-04-01) Kayalıgil, Sinan; Özdemirel, Nur Evin; İnkaya, Tulin; İNKAYA, TÜLİN; 0000-0002-6260-0162; AAH-2155-2021; AAZ-8000-2020Boundary extraction is a fundamental post-clustering problem. It facilitates interpretability and usability of clustering results. Also, it provides visualization and dataset reduction. However, it has not attracted much attention compared to the clustering problem itself. In this work, we address the boundary extraction of clusters in 2- and 3-dimensional spatial datasets. We propose two algorithms based on Delaunay Triangulation (DT). Numerical experiments show that the proposed algorithms generate the cluster boundaries effectively. Also, they yield significant amounts of dataset reduction.Publication Characterization of syrian refugees with work permit applications in Turkey: A data mining based methodology(Elsevier, 2021-05-15) Gençosman, Burcu Çağlar; İnkaya, Tülin; ÇAĞLAR GENÇOSMAN, BURCU; İNKAYA, TÜLİN; Bursa Uludağ Üniversitesi/Endüstri Mühendisliği Bölümü; 0000-0003-0159-8529; 0000-0002-6260-0162; AAH-2155-2021; AAG-8600-2021With the technological advancements in data collection systems, data-driven approaches become a necessity for understanding and managing the socioeconomic systems. Motivated by this, we focus on the formal employment of Syrian refugees in Turkey, and propose a data mining based methodology in order to understand their profiles. In this context, Syrian refugees with work permit applications are examined between years 2010 and 2018. The dataset includes demographic properties of the applicants and characteristics of their workplaces. The proposed methodology aims to extract the hidden, interesting and useful characteristics of the Syrian refugees having formal employment potential. The proposed approach integrates several data mining tasks, i.e. clustering, classification, and association rule mining, and it has four phases. In the first phase, data pre-processing and visualization operations are performed. In the second phase, the profiles of the Syrian refugee workers are determined using clustering. Self-organizing map and hierarchical clustering are implemented for this purpose. In the third phase, decision tree is used to specify the distinguishing characteristics of the clusters. In the fourth phase, the association rules are generated to reveal the interesting and frequent properties of each cluster. The results reveal the profiles of Syrian refugees with work permit applications. The findings obtained from this study can be a basis for developing policies and strategies that facilitate the labor market integration of the immigrants. The proposed methodology can be used to analyze time-dependent patterns and other immigration data for different countries as well.