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Auflistung Nach Schlagwort "High dimensional data"

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    • Clustering of high dimensional data streams 

      Tasoulis, S. K.; Tasoulis, D. K.; Plagianakos, V. P. (2012)
      Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex ...

    • Density based projection pursuit clustering 

      Tasoulis, S. K.; Epitropakis, M. G.; Plagianakos, V. P.; Tasoulis, D. K. (2012)
      Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower ...

    • Efficient change detection for high dimensional data streams 

      Georgakopoulos S.V., Tasoulis S.K., Plagianakos V.P. (2015)
      The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high ...

    • Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces 

      Vrahatis A.G., Dimitrakopoulos G.N., Tasoulis S.K., Plagianakos V.P. (2019)
      We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex ...

    • Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm 

      Chatzilygeroudis K.I., Vrahatis A.G., Tasoulis S.K., Vrahatis M.N. (2021)
      Big data methods prevail in the biomedical domain leading to effective and scalable data-driven approaches. Biomedical data are known for their ultra-high dimensionality, especially the ones coming from molecular biology ...

    • RLAC: Random Line Approximation Clustering 

      Barbas P., Vrahatis A.G., Tasoulis S.K. (2021)
      We explore how Random Projections can be used as an Approximate method for Projection Pursuit Clustering in high dimensional data. Traditional data transformations such as PCA for dimensionality reduction have been shown ...

    • Supervised papers classification on large-scale high-dimensional data with apache spark 

      Akritidis L., Bozanis P., Fevgas A. (2018)
      The problem of classifying a research article into one or more fields of science is of particular importance for the academic search engines and digital libraries. A robust classification algorithm offers the users a wide ...

    • Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances 

      Vrahatis A.G., Tasoulis S.K., Dimitrakopoulos G.N., Plagianakos V.P. (2019)
      The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA ...