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1. Using Greedy algorithm: DBSCAN revisited II

   The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN)...

   Shi-hong Yue, ** Li, ... Shui-geng Zhou in Journal of Zhejiang University-SCIENCE A

   Article 01 November 2004

2. Experimental Modelling (Identification)

   In Chapter 4, theoretical models of hydraulic servo-systems have been presented and analysed. The subsystem models of hydraulic servo-systems have...

   Mohieddine Jelali, Andreas Kroll in Hydraulic Servo-systems

   Chapter 2003
   

3. Some Notes on Alternating Optimization

   Let f : ℜs ↦ ℜ be a real-valued scalar field, and let x = (x1,…, xs)T ∈ ℜs be partitioned into t subsets of non-overlap** variables as x = (X1,…,Xt...

   James C. Bezdek, Richard J. Hathaway in Advances in Soft Computing — AFSS 2002

   Conference paper 2002
   

4. Maximum-entropy clustering algorithm and its global convergence analysis

   Constructing a batch of differentiable entropy functions to uniformly approximate an objective function by means of the maximum-entropy principle, a...

   Zhang Zhihua, Zheng Nanning, Shi Gang in Science in China Series E: Technological Sciences

   Article 01 February 2001

5. Probabilistische und Fuzzy Methoden für die Clusteranalyse

   Ausgehend von der probabilistischen Clusteranalyse mit dem Fuzzy c-Means (FCM) Modell lassen sich Methoden der nichtprobabilistischen Clusteranalyse...

   Thomas A. Runkler in Fuzzy Theorie und Stochastik

   Chapter 1999
   

6. Ergänzende Literatur

   Hans-Jürgen Zimmermann in Neuro + Fuzzy

   Chapter 1995
   

7. In-Time Failure Prognosis and Fatigue Life Prediction of Structures

   The in-time failure prognosis and safety assessment of today’s high risk industrial structures implies the accurate estimation of the residual...

   A. D. Pouliezos, G. S. Stavrakakis in Real Time Fault Monitoring of Industrial Processes

   Chapter 1994