Now showing items 1-20 of 29

    • Active and mobile data management through event history mining 

      Saygın, Yücel (Bilkent University, 2001-08)
    • Adaptive ensemble learning with confidence bounds for personalized diagnosis 

      Tekin, C.; Yoon, J.; Schaar, M. V. D. (AI Access Foundation, 2016)
      With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in ...
    • Application of map/reduce paradigm in supercomputing systems 

      Demirci, Gündüz Vehbi (Bilkent University, 2013)
      Map/Reduce is a framework first introduced by Google in order to rapidly develop big data analytic applications on distributed computing systems. Even though the Map/Reduce paradigm had a game changing impact on certain ...
    • Association rules for supporting hoarding in mobile computing environments 

      Saygın, Yücel; Ulusoy, Özgür; Elmagarmid, A. K. (IEEE, 2000)
      One of the features that a mobile computer should provide is disconnected operation which is performed by hoarding. The process of hoarding can be described as loading the data items needed in the future to the client cache ...
    • Cell-graph mining for breast tissue modeling and classification 

      Bilgin, C.; Demir, Çiğdem; Nagi, C.; Yener, B. (IEEE, 2007-08)
      We consider the problem of automated cancer diagnosis in the context of breast tissues. We present graph theoretical techniques that identify and compute quantitative metrics for tissue characterization and classification. ...
    • Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining 

      Aljobouri, H. K.; Jaber, H. A.; Koçak, O. M.; Algin, O.; Çankaya, I. (Elsevier, 2018)
      Background: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the ...
    • Competitive and online piecewise linear classification 

      Özkan, Hüseyin; Donmez, M.A.; Pelvan O.S.; Akman, A.; Kozat, Süleyman S. (IEEE, 2013)
      In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a ...
    • A constraint-based incremental approach for update of large itemsets 

      Demir, Engin (Bilkent University, 2001-08)
    • Çağrı merkezi metin madenciliği yaklaşımı 

      Yigit, I. O.; Ates, A. F.; Guvercin, M.; Ferhatosmanoglu, H.; Gedik, B. (Institute of Electrical and Electronics Engineers Inc., 2017)
      Günümüzde çağrı merkezlerindeki görüşme kayıtlarının sesten metne dönüştürülebilmesi görüşme kaydı metinleri üzerinde metin madenciliği yöntemlerinin uygulanmasını mümkün kılmaktadır. Bu çalışma kapsamında görüşme ...
    • A data mining approach for location prediction in mobile environments 

      Yavaş G.; Katsaros, D.; Ulusoy, Ö.; Manolopoulos, Y. (Elsevier, 2005)
      Mobility prediction is one of the most essential issues that need to be explored for mobility management in mobile computing systems. In this paper, we propose a new algorithm for predicting the next inter-cell movement ...
    • Data mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’ 

      Okutucu, S.; Katircioglu-Öztürk, D.; Oto, E.; Güvenir, H. A.; Karaagaoglu, E.; Oto, A.; Meinertz, T.; Goette, A. (Oxford University Press, 2016)
      Aims: The aims of this study include (i) pursuing data-mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial dataset containing atrial fibrillation (AF) burden scores ...
    • Discovering story chains: a framework based on zigzagged search and news actors 

      Toraman C.; Can, F. (John Wiley and Sons Inc., 2017)
      A story chain is a set of related news articles that reveal how different events are connected. This study presents a framework for discovering story chains, given an input document, in a text collection. The framework has ...
    • A discretization method based on maximizing the area under receiver operating characteristic curve 

      Kurtcephe, M.; Güvenir H. A. (World Scientific Publishing Co. Pte. Ltd., 2013)
      Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the ...
    • Diversity and novelty in web search, recommender systems and data streams 

      Santos, R. L. T.; Castells, P.; Altingovde, I. S.; Can, Fazlı (Association for Computing Machinery, 2014-02)
      This tutorial aims to provide a unifying account of current research on diversity and novelty in the domains of web search, recommender systems, and data stream processing.
    • Effective early termination techniques for text similarity join operator 

      Özalp, S. A.; Ulusoy, Özgür (Springer, Berlin, Heidelberg, 2005)
      Text similarity join operator joins two relations if their join attributes are textually similar to each other, and it has a variety of application domains including integration and querying of data from heterogeneous ...
    • First large-scale information retrieval experiments on Turkish texts 

      Can, Fazlı; Koçberber, Seyit; Balcık, Erman; Kaynak, Cihan; Öcalan, H. Çağdaş; Vursavaş, Onur M. (ACM, 2006-08)
      We present the results of the first large-scale Turkish information retrieval experiments performed on a TREC-like test collection. The test bed, which has been created for this study, contains 95.5 million words, 408,305 ...
    • Free riding in peer-to-peer networks 

      Karakaya, M.; Korpeoglu, I.; Ulusoy, Ö. (Institute of Electrical and Electronics Engineers, 2009)
      Free riding in peer-to-peer (P2P) networks poses a serious threat to their proper operation. Here, the authors present a variety of approaches developed to overcome this problem. They introduce several unique aspects of ...
    • Generating time-varying road network data using sparse trajectories 

      Eser, E.; Kocayusufoglu, F.; Eravci, B.; Ferhatosmanoglu, H.; Larriba-Pey, J. L. (IEEE Computer Society, 2017)
      While research on time-varying graphs has attracted recent attention, the research community has limited or no access to real datasets to develop effective algorithms and systems. Using noisy and sparse GPS traces from ...
    • A large-scale sentiment analysis for Yahoo! Answers 

      Küçüktunç, O.; Cambazoğlu, B. B.; Weber, I.; Ferhatosmanoğlu, Hakan (ACM, 2012)
      Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative ...
    • Location recommendations for new businesses using check-in data 

      Eravci, B.; Bulut, N.; Etemoglu, C.; Ferhatosmanoglu, H. (IEEE Computer Society, 2017)
      Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits ...