'Good clustering' diaporamas de présentation

Machine Learning & Data Mining

Machine Learning & Data Mining. What is Machine Learning?. a branch of artificial intelligence, concerns the construction and study of systems that can learn from data .

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unsupervised learning - clustering

Inter-cluster distances are maximized. Intra-cluster distances are minimized. unsupervised learning - clustering. Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

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Clustering Analysis of Spatial Data Using pTrees (pTree technology is patented by NDSU)

Clustering Analysis of Spatial Data Using pTrees (pTree technology is patented by NDSU). Introduction. Existing methods are not suitable for cluster analysis due to the large size. Peano Count Tree (PC-tree) provides a lossless, compressed representation of a spatial dataset.

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Descriptive Modeling

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What is Cluster Analysis?

Inter-cluster distances are maximized. Intra-cluster distances are minimized. What is Cluster Analysis?. Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups.

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Motivation

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Data Mining and Warehousing: Chapter 8

Data Mining and Warehousing: Chapter 8. Cluster Analysis Jia-wei Han http://www.cs.sfu.ca/~han. Clustering analysis. What is Clustering Analysis ? Clustering in Data Mining Applications Handling Different Types of Variables Major Clustering Techniques Outlier Discovery

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Cluster Analysis

Cluster Analysis. Cluster Analysis. What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods Density-Based Methods Grid-Based Methods Subspace Clustering/Bi-clustering Model-Based Clustering.

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Combinatorial and algebraic tools for multigrid

Combinatorial and algebraic tools for multigrid. Yiannis Koutis Computer Science Department Carnegie Mellon University. multilevel methods. www.mgnet.org 3500 citations 25 free software packages 10 special conferences since 1983 Algorithms not always working

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What is Cluster Analysis?

What is Cluster Analysis?. Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping a set of data objects into clusters Clustering is unsupervised classification : no predefined classes

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Lecture 10 Clustering

Lecture 10 Clustering. Preview. Introduction Partitioning methods Hierarchical methods Model-based methods Density-based methods. Examples of Clustering Applications.

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What is Cluster Analysis?

What is Cluster Analysis?. Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping a set of data objects into clusters Clustering is unsupervised classification : no predefined classes

471 views • 38 slides

Clustering: Partition Clustering

Clustering: Partition Clustering. Lecture outline. Distance/Similarity between data objects Data objects as geometric data points Clustering problems and algorithms K-means K-median K-center. Inter-cluster distances are maximized. Intra-cluster distances are minimized.

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Clustering

Clustering. Data Mining Overview. Data Mining Data warehouses and OLAP (On Line Analytical Processing.) Association Rules Mining Clustering: Hierarchical and Partitional approaches Classification: Decision Trees and Bayesian classifiers Sequential Patterns Mining

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Clustering

Clustering. Data Mining Overview. Data Mining Data warehouses and OLAP (On Line Analytical Processing.) Association Rules Mining Clustering: Hierarchical and Partitional approaches Classification: Decision Trees and Bayesian classifiers Sequential Patterns Mining

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The Stability of a Good Clustering

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What is Cluster Analysis

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Cube Tree

Cube Tree. Dimension: number of group-by values Relation tuples map to a point in the space Aggregates: projection of all data points on all the subspaces. Intersection between a subspace and the orthogonal hyper-plane stores the aggregates. Origin represents aggregate with no grouping

453 views • 9 slides