Topic 5
... Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms ...
... Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms ...
Data Mining
... • Merging-based (bottom-up) vs. splitting-based methods • Merge: Find the best neighboring intervals and merge them to form larger intervals recursively • ChiMerge [Kerber AAAI 1992, See also Liu et al. DMKD 2002] – Initially, each distinct value of a numerical attr. A is considered to be one interv ...
... • Merging-based (bottom-up) vs. splitting-based methods • Merge: Find the best neighboring intervals and merge them to form larger intervals recursively • ChiMerge [Kerber AAAI 1992, See also Liu et al. DMKD 2002] – Initially, each distinct value of a numerical attr. A is considered to be one interv ...
comparative analysis of parallel k means and parallel fuzzy c means
... computation. In contrast, parallel computing makes use of more than one central processing unit at the same time in order to allow users to complete lengthy computational tasks more quickly. Parallel computing differs from multi tasking where a single processor gives the appearance of working on two ...
... computation. In contrast, parallel computing makes use of more than one central processing unit at the same time in order to allow users to complete lengthy computational tasks more quickly. Parallel computing differs from multi tasking where a single processor gives the appearance of working on two ...
parameter-free cluster detection in spatial databases and its
... The first basic step is the computation of the Delaunay Triangulation (DT) from a given set of points. In the next step we compute first the Gabriel Graph (GG) from the DT, second the Relative Neighborhood Graph (RNG) from the GG, and third the Nearest Neighbor Graph (NNG) from the RNG (figure 1a)). ...
... The first basic step is the computation of the Delaunay Triangulation (DT) from a given set of points. In the next step we compute first the Gabriel Graph (GG) from the DT, second the Relative Neighborhood Graph (RNG) from the GG, and third the Nearest Neighbor Graph (NNG) from the RNG (figure 1a)). ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.