'Closest centroid' diaporamas de présentation

Chapter 4: Unsupervised Learning

Chapter 4: Unsupervised Learning. Road map. Basic concepts K-means algorithm Representation of clusters Hierarchical clustering Distance functions Data standardization Handling mixed attributes Which clustering algorithm to use? Cluster evaluation Discovering holes and data regions

3.47k views • 95 slides

Clustering

Clustering. Idea and Applications. Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. It is also called unsupervised learning. It is a common and important task that finds many applications. Applications in Search engines:

592 views • 46 slides

Microarrays

Microarrays. Technology behind microarrays Data analysis approaches Clustering microarray data. Molecular biology overview . Nucleus. Cell. Chromosome. Gene (DNA). Gene (mRNA), single strand. Protein. Graphics courtesy of the National Human Genome Research Institute. Gene expression.

598 views • 42 slides

Birch: Balanced Iterative Reducing and Clustering using Hierarchies

Birch: Balanced Iterative Reducing and Clustering using Hierarchies. By Tian Zhang, Raghu Ramakrishnan. Presented by Vladimir Jeli? 3218 /10 e-mail: jelicvladimir5@gmail.com. What is Data Clustering?. A cluster is a closely-packed group.

605 views • 33 slides

Unsupervised Learning

Unsupervised Learning. Supervised learning vs. unsupervised learning. Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm . Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm . Adapted from Andrew Moore, http://www.autonlab.org/tutorials/gmm .

425 views • 31 slides

Birch: An efficient data clustering method for very large databases

439 views • 26 slides

Web Document Clustering

Web Document Clustering. Department of Computer Science and Engineering Southern Methodist University Wenyi Ni. Why web document clustering is needed? . 3.3 billion web pages on the internet Every time you post a query, the search engine returns thousands of records.

398 views • 21 slides

Presenter : Keng -Yu Lin Author : Amir Ahmad , Lipika Dey PRL . 2011

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets. Presenter : Keng -Yu Lin Author : Amir Ahmad , Lipika Dey PRL . 2011. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

281 views • 12 slides

Unsupervised Learning: Clustering

418 views • 9 slides