'Closest clusters' presentation slideshows

Closest clusters - PowerPoint PPT Presentation

Unsupervised learning & Cluster Analysis: Basic Concepts and Algorithms

Unsupervised learning & Cluster Analysis: Basic Concepts and Algorithms

Unsupervised learning & Cluster Analysis: Basic Concepts and Algorithms. Assaf Gottlieb. Some of the slides are taken form Introduction to data mining, by Tan, Steinbach, and Kumar. What is unsupervised learning & Cluster Analysis ?.

★ ★ ★ ★ ★

1.36k views • 92 slides
Clustering Basic Concepts and Algorithms 2

Clustering Basic Concepts and Algorithms 2

Clustering Basic Concepts and Algorithms 2. Clustering topics. Hierarchical clustering Density-based clustering Cluster validity. Proximity measures. Proximity is a generic term that refers to either similarity or dissimilarity. Similarity

★ ★ ★ ★ ★

801 views • 62 slides
Clustering Techniques for Finding Patterns in Large Amounts of Biological Data

Clustering Techniques for Finding Patterns in Large Amounts of Biological Data

Clustering Techniques for Finding Patterns in Large Amounts of Biological Data. Michael Steinbach Department of Computer Science steinbac@cs.umn.edu www.cs.umn.edu/~kumar. Inter-cluster distances are maximized. Intra-cluster distances are minimized. Clustering.

★ ★ ★ ★ ★

626 views • 62 slides
Clustering II

Clustering II

Clustering II. Hierarchical Clustering . Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

★ ★ ★ ★ ★

532 views • 38 slides
Clustering II

Clustering II

Clustering II. Hierarchical Clustering . Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. No assumptions on the number of clusters

★ ★ ★ ★ ★

502 views • 35 slides
Clustering (2)

Clustering (2)

Clustering (2). Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

★ ★ ★ ★ ★

508 views • 33 slides
Clustering (2)

Clustering (2)

Clustering (2). Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

★ ★ ★ ★ ★

336 views • 33 slides
Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering. Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

★ ★ ★ ★ ★

1.16k views • 32 slides
Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Do not have to assume any particular number of clusters

★ ★ ★ ★ ★

778 views • 29 slides
CSE 980: Data Mining

CSE 980: Data Mining

CSE 980: Data Mining. Lecture 16: Hierarchical Clustering. Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

★ ★ ★ ★ ★

411 views • 29 slides
Machine Learning and Data Mining Clustering

Machine Learning and Data Mining Clustering

+. Machine Learning and Data Mining Clustering. (adapted from) Prof. Alexander Ihler. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A. Overview. What is clustering and its applications? Distance between two clusters.

★ ★ ★ ★ ★

639 views • 28 slides
Non-linear DA and Clustering

Non-linear DA and Clustering

Non-linear DA and Clustering. Stat 600. Nonlinear DA. We discussed LDA where our discriminant boundary was linear Now, lets consider scenarios where it could be non-linear We will discuss: QDA RDA MDA As before all these methods aim to MINIMIZE the probability of misclassification. QDA.

★ ★ ★ ★ ★

417 views • 28 slides
Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering. Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Do not have to assume any particular number of clusters

★ ★ ★ ★ ★

540 views • 27 slides
Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering. Week 6. Outline. Introduction Cluster Distance Measures Agglomerative Algorithm Example Labelling and Quality of Clusters Conclusions. Introduction . Hierarchical Clustering Approach

★ ★ ★ ★ ★

775 views • 26 slides
Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering . Week 7 - 1. Outline. Introduction Cluster Distance Measures Agglomerative Algorithm Example Labelling and Quality of Clusters Conclusions. Introduction . Hierarchical Clustering Approach

★ ★ ★ ★ ★

552 views • 25 slides
Microarray Gene Expression Data Analysis

Microarray Gene Expression Data Analysis

Microarray Gene Expression Data Analysis. Functional Genomics. Chapter: 07. A.Venkatesh CBBL. Contents. Introduction. Microarray technology provides a powerful tool that enables researchers to observe simultaneously mRNA expression levels of thousands of genes .

★ ★ ★ ★ ★

418 views • 17 slides

View Closest clusters PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Closest clusters PowerPoint presentations. You can view or download Closest clusters presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.