R Reference Card for Data Mining
... rpartOrdinal ordinal classification trees, deriving a classification tree when the response to be predicted is ordinal ...
... rpartOrdinal ordinal classification trees, deriving a classification tree when the response to be predicted is ordinal ...
comparative investigations and performance analysis of
... Clustering can be considered the most important unsupervised learning problem. So, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are simil ...
... Clustering can be considered the most important unsupervised learning problem. So, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are simil ...
Methods and Algorithms of Time Series Processing in
... distributions is being solved, and a new vector Θ approximation is computed. In the case when all mixture components have normal (Gaussian) probability density, one can represent the solution analytically. That is why mixtures of normal distributions (GMM - Gaussian Mixture Model) are used commonly ...
... distributions is being solved, and a new vector Θ approximation is computed. In the case when all mixture components have normal (Gaussian) probability density, one can represent the solution analytically. That is why mixtures of normal distributions (GMM - Gaussian Mixture Model) are used commonly ...
Lecture 3
... Start with single-instance clusters At each step, join the two closest clusters Method to compute distance between cluster x and y: single linkage (distance between closest point in cluster x and y), average linkage (average distance between all points), complete linkage (distance between furthest p ...
... Start with single-instance clusters At each step, join the two closest clusters Method to compute distance between cluster x and y: single linkage (distance between closest point in cluster x and y), average linkage (average distance between all points), complete linkage (distance between furthest p ...
An Efficient Supervised Document Clustering
... The process of Affinity Propagation can be viewed as a message passing process with two kinds of messages exchanged among data points: responsibility and availability. Responsibility, r[i, j], is a message from data point i to j that reflects the accumulated evidence for how well-suited data point j ...
... The process of Affinity Propagation can be viewed as a message passing process with two kinds of messages exchanged among data points: responsibility and availability. Responsibility, r[i, j], is a message from data point i to j that reflects the accumulated evidence for how well-suited data point j ...
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.