'Means clustering algorithm' presentation slideshows

Week 7 Video 1

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Pattern Recognition: Statistical and Neural

Nanjing University of Science & Technology. Pattern Recognition: Statistical and Neural. Lonnie C. Ludeman Lecture 26 Nov 4, 2005. Lecture 26 Topics. General Concept of Clustering Basic problems in determining clusters Definition of distance functions between clusters

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Unsupervised Learning

Unsupervised Learning. Clustering K-Means. Recall: Key Components of Intelligent Agents. Representation Language: Graph, Bayes Nets, Linear functions Inference Mechanism: A*, variable elimination, Gibbs sampling

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Given a set of data points as input Randomly assign each point to one of the k clusters

k -means Clustering Algorithm. Given a set of data points as input Randomly assign each point to one of the k clusters Repeat until convergence Calculate model of each of the k clusters Assign each point to the cluster with the closest model. k -means Clustering Example.

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Delta-MSE D issimilarity in Suboptimal K-Means Clusterin g

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Clustering

Clustering. Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data. The example below demonstrates the clustering of balls of same colour. There are a total of 10 balls which are of three different

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LECTURE 31 : CLUSTERING

LECTURE 31 : CLUSTERING. Objectives: Mixture Densities Maximum Likelihood Estimates Application to Gaussian Mixture Models k-Means Clustering Fuzzy k- Means Clustering

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LECTURE 27: CLUSTERING

LECTURE 27: CLUSTERING. Objectives: Mixture Densities Maximum Likelihood Estimates Application to Gaussian Mixture Models k-Means Clustering Fuzzy k- Means Clustering

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Categorical K-means Clustering Algorithm

Categorical K-means Clustering Algorithm. Graduate : Chen, Shao-Pei Authors : C.C. Hsu TKDE. Outline. Motivation Objective Methodology Experimental Results Conclusion Appendix. Motivation. The k-means algorithm works only for numerical data

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Categorical K-means Clustering Algorithm

Categorical K-means Clustering Algorithm. Graduate : Chen, Shao-Pei Authors : C.C. Hsu TKDE. Outline. Motivation Objective Methodology Experimental Results Conclusion Appendix. Motivation. The k-means algorithm works only for numerical data

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AIM: Clustering the Data together

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Face Recognition based on Radial Basis Function and Clustering Algorithm

Face Recognition based on Radial Basis Function and Clustering Algorithm. Yuanfeng Gao 2008/12/12. Face Recognition task. Various methods Neural Network approach: - Can recessively express many rules for face recognition and has much stronger adaptability by training networks. Part I.

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A new segmentation algorithm for medical volume image based on K-means clustering

K-means algorithm is wildly used in medical image segmentation for its powerful fuzzy information process ability but the algorithm has some shortages such as low efficiency in calculation which limited the usage of the algorithm. Some measures are advanced to overcome the shortages of original K-means algorithm and a new medical volume image segmentation algorithm is presented. Firstly, according to the physical means of the medical data, the volume data field is preprocessed to speed up succeed clustering processing; Secondly, the improved K-means algorithm is deduced and analyzed through improving cluster seed selection method and calculation flow and redesigning pixel processing and operational principle of original K-means algorithm. Finally, the experimental results show that the algorithm has high accuracy when used to segment 3D medical images and can improve calculation speed greatly.

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