Search results
Results From The WOW.Com Content Network
Every data mining task has the problem of parameters. Every parameter influences the algorithm in specific ways. For DBSCAN, the parameters ε and minPts are needed. The parameters must be specified by the user. Ideally, the value of ε is given by the problem to solve (e.g. a physical distance), and minPts is then the desired minimum cluster ...
The image above illustrates this concept. In its upper left area, a synthetic example data set is shown. The upper right part visualizes the spanning tree produced by OPTICS, and the lower part shows the reachability plot as computed by OPTICS. Colors in this plot are labels, and not computed by the algorithm; but it is well visible how the ...
For example, a point at a "small" distance to a very dense cluster is an outlier, while a point within a sparse cluster might exhibit similar distances to its neighbors. While the geometric intuition of LOF is only applicable to low-dimensional vector spaces, the algorithm can be applied in any context a dissimilarity function can be defined.
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
For the following definitions, two examples will be used. The first is the problem of character recognition given an array of bits encoding a binary-valued image. The other example is the problem of finding an interval that will correctly classify points within the interval as positive and the points outside of the range as negative.
The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web.
Behnke relied only on the sign of the gradient when training his Neural Abstraction Pyramid [21] to solve problems like image reconstruction and face localization. [ citation needed ] Neural networks can also be optimized by using a universal search algorithm on the space of neural network's weights, e.g., random guess or more systematically ...
mlpack contains a wide range of algorithms that are used to solved real problems from classification and regression in the Supervised learning paradigm to clustering and dimension reduction algorithms. In the following, a non exhaustive list of algorithms and models that mlpack supports: Collaborative Filtering; Decision stumps (one-level ...