'Rand index' diaporamas de présentation

Clustering Techniques for Information Retrieval

Clustering Techniques for Information Retrieval. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References:

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???????????????????? Face Recognition with Local Binary Patterns and Partial Matching

???????????????????? Face Recognition with Local Binary Patterns and Partial Matching. Presenter : ??? Advisor : ??? ??. Outlines. Motivation Implementation Result Conclusion. Motivation. Motivation. Publication.

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CSE 482: Big Data Analysis

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Principal Component Analysis (PCA) for Clustering Gene Expression Data

Principal Component Analysis (PCA) for Clustering Gene Expression Data. K. Y. Yeung and W. L. Ruzzo. Outline. Motivation of PCA and this paper Approach of this paper Data sets Clustering algorithms and similarity metrics Results and discussion. Why Are We Interested in PCA?.

534 views • 33 slides

Principal Component Analysis (PCA) for Clustering Gene Expression Data

Principal Component Analysis (PCA) for Clustering Gene Expression Data. K. Y. Yeung and W. L. Ruzzo. Organization. Association of PCA and this paper Approach of this paper Data sets Clustering algorithms and similarity metrics Results and discussion. The Functions of PCA?.

508 views • 33 slides

Cluster Analysis III

Cluster Analysis III. 10/5/2012. Outline. Estimate the number of clusters. Evaluation of clustering results. Estimate the number of clusters. Milligan & Cooper(1985) compared over 30 published rules. None is particularly better than all the others. The best method is data dependent.

442 views • 30 slides

Cluster Evaluation

Cluster Evaluation. Metrics that can be used to evaluate the quality of a set of document clusters. Precision Recall & FScore. From Zhao and Karypis, 2002 These metrics are computed for every (class,cluster) pair. Terms: class L r of size n r cluster S i if size n i

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114 views • 10 slides