Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 4Survey: Running and Comparing Stream Clustering Algorithms(CEUR Workshop Proceedings, 2018) Ahmed, Rowanda D.; Dalkılıç, Gökhan; Erten, MuratRecently, clustering data streams have become an incredibly important research area for knowledge discovery as applications produce more and more unstoppable streaming data. In this paper we introduce clustering, streams and data streaming clustering algorithms, as well as discussions of the most important stream clustering algorithms, considering their structure. As an additional contribution of our work and differently from review and survey papers in stream clustering, we offer the practical part of the most known stream clustering algorithms, namely: (i) CluStream; (ii) DenStream; (iii) D-Stream; and (iv) ClusTree, showing their experimental results along with some performance metrics computation of for each, depending on MOA framework.Conference Object A Data Coding and Screening System for Accident Risk Patterns: A Learning System(WITPress, 2011) Geçer Sargın, Feral; Geçer Sargın, Feral; Duvarcı, Yavuz; Duvarcı, Yavuz; İnan, E.; İnan, E.; Kumova, Bora İsmail; Kumova, Bora İsmail; Atay Kaya, İlgi; Atay Kaya, İlgiAccidents on urban roads can occur for many reasons, and the contributing factors together pose some complexity in the analysis of the casualties. In order to simplify the analysis and track changes from one accident to another for comparability, an authentic data coding and category analysis methods are developed, leading to data mining rules. To deal with a huge number of parameters, first, most qualitative data are converted into categorical codes (alpha-numeric), so that computing capacity would also be increased. Second, the whole data entry per accident are turned into ID codes, meaning each crash is possibly unique in attributes, called 'accident combination', reducing the large number of similar value accident records into smaller sets of data. This genetical code technique allows us to learn accident types with its solid attributes. The learning (output averages) provides a decision support mechanism for taking necessary cautions for similar combinations. The results can be analyzed by inputs, outputs (attributes), time (years) and the space (streets). According to Izmir's case results; sampled data and its accident combinations are obtained for 3 years (2005 - 2007) and their attributes are learned. © 2011 WIT Press.