Segmentation with Matlab. Cluster Analisis and Nearest Neighbors (Knn)
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Home > Mathematics and Science Textbooks > Mathematics > Probability and statistics > Segmentation with Matlab. Cluster Analisis and Nearest Neighbors (Knn)
Segmentation with Matlab. Cluster Analisis and Nearest Neighbors (Knn)

Segmentation with Matlab. Cluster Analisis and Nearest Neighbors (Knn)


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About the Book

Cluster analisys is a set of unsupervised learning techniques to find natural groupings and patterns in data. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.Cluster analysis, also called segmentation analysis or taxonomy analysis, partitions sample data into groups or clusters. Clusters are formed such that objects in the same cluster are very similar, and objects in different clusters are very distinct. MATLAB Statistics and Machine Learning Toolbox provides several clustering techniques and measures of similarity (also called distance measures) to create the clusters. Additionally, cluster evaluation determines the optimal number of clusters for the data using different evaluation criteria. Cluster visualization options include dendrograms and silhouette plots.Gaussian mixture models (GMM) are often used for data clustering. Usually, fitted GMMs cluster by assigning query data points to the multivariate normal components that maximize the component posterior probability given the data. Nearest neighbor search locates the k closest observations to the specified data points, based on your chosen distance measure. Available distance measures include Euclidean, Hamming, Mahalanobis, and more.


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Product Details
  • ISBN-13: 9781091196360
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 210
  • Spine Width: 12 mm
  • Width: 152 mm
  • ISBN-10: 1091196362
  • Publisher Date: 21 Mar 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Weight: 313 gr


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Segmentation with Matlab. Cluster Analisis and Nearest Neighbors (Knn)
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