9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Book Chapter: not found
      Swarm Intelligence in Data Mining 

      Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection

      other
      ,
      Springer Berlin Heidelberg

      Read this book at

      Buy book Bookmark
          There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references26

          • Record: found
          • Abstract: not found
          • Article: not found

          A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

          J. C. Dunn (1973)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources.

            An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast and robust fixed-point algorithms for independent component analysis.

              Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
                Bookmark

                Author and book information

                Book Chapter
                2006
                : 101-123
                10.1007/978-3-540-34956-3_5
                92a05d0e-36ca-4a44-9d8c-d0f73c4bc19d
                History

                Comments

                Comment on this book

                Book chapters

                Similar content1,317

                Cited by4