Subspace Clustering for Complex Data
... step of the KDD process: data mining. Out of the several mining tasks that exist in the literature, this work centers on the important method of clustering, which aims at grouping similar objects while separating dissimilar ones. Clustering, as an unsupervised learning task, analyses data without gi ...
... step of the KDD process: data mining. Out of the several mining tasks that exist in the literature, this work centers on the important method of clustering, which aims at grouping similar objects while separating dissimilar ones. Clustering, as an unsupervised learning task, analyses data without gi ...
3 - School of Computer Science and Software Engineering
... IPAM Tutorial-January 2002-Vipin Kumar ...
... IPAM Tutorial-January 2002-Vipin Kumar ...
pptx
... people run it multiple times with different random initializations, and choose the best result. In some cases, K-means will still take exponential time (assuming P!=NP), even to find a local minimum. However, such cases are rare in practice. ...
... people run it multiple times with different random initializations, and choose the best result. In some cases, K-means will still take exponential time (assuming P!=NP), even to find a local minimum. However, such cases are rare in practice. ...
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.