There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design,...
There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World W...
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.
Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and...
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to disc...
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.
Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and...
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to disc...
A fundamental problem in control theory is concerned with the stability of a given linear system. The design of a control system is generally based on a simplified model. The true values of the physical parameters may differ from the assumed values. Robust Stability and Convexity addresses stability problems for linear systems with parametric uncertainty. The application of convexity techniques leads to new computationally tractable stability criteria for families of characteristic functions with nonlinear dependence on the parameters. Stability results as well as stability...
A fundamental problem in control theory is concerned with the stability of a given linear system. The design of a control system is generally based on...
