Measures of Clustering Quality: A Working Set of Axioms for Clustering

Dec 14, 2023

240 likes | 274 Vues

Explore how measures of clustering quality can bridge the gap between theoretical concepts and practical applications in various fields such as social sciences, biology, astronomy, and computer science.

Share Presentation

Embed Code

kalinda

Télécharger la présentation

Measures of Clustering Quality: A Working Set of Axioms for Clustering

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Clustering

Clustering. CMPUT 466/551 Nilanjan Ray. What is Clustering?. Attach label to each observation or data points in a set You can say this “unsupervised classification” Clustering is alternatively called as “grouping”

709 views • 34 slides

Clustering Preliminaries

Clustering Preliminaries. Applications Euclidean/Non-Euclidean Spaces Distance Measures. The Problem of Clustering.

608 views • 34 slides

Automatic Clustering & Classification

Automatic Clustering & Classification. Team : Yang Priyanka Jithesh Arun. Agenda. Introduction to Clustering and Categorization. Types of Clustering Application of Clustering Application of Categorization Example (Quintara, NCSU Libraries)

576 views • 25 slides

Clustering II

Clustering II. CMPUT 466/551 Nilanjan Ray. Mean-shift Clustering. Will show slides from: http://www.wisdom.weizmann.ac.il/~deniss/vision_spring04/files/mean_shift/mean_shift.ppt. Spectral Clustering. Let’s visit a serious issue with K -means

205 views • 10 slides

Clustering III

Clustering III. Lecture outline. Soft (model-based) clustering and EM algorithm Clustering aggregation [A. Gionis , H. Mannila , P. Tsaparas : Clustering aggregation, ICDE 2004] Impossibility theorem for clustering [Jon Kleinberg, An impossibility theorem for clustering, NIPS 2002].

414 views • 38 slides

Agenda

Agenda. Introduction to clustering Dissimilarity measure Preprocessing Clustering method Hierarchical clustering K-means and K-memoids Self-organizing maps (SOM) Model-based clustering Estimate # of clusters Two new methods allowing scattered genes 4.1 Tight clustering

759 views • 54 slides

Collaborative Clustering for Entity Clustering

Collaborative Clustering for Entity Clustering. Zheng Chen and Heng Ji Computer Science Department and Linguistics Department Queens College and Graduate Center City University of New York November 5, 2012. Outline. Entity clustering and NIL entity clustering

462 views • 28 slides

Lecture outline

Lecture outline. Clustering aggregation Reference: A . Gionis , H. Mannila , P. Tsaparas : Clustering aggregation, ICDE 2004 Co-clustering (or bi-clustering) References: A. Anagnostopoulos , A. Dasgupta and R. Kumar: Approximation Algorithms for co-clustering, PODS 2008.

447 views • 31 slides

Clustering

Clustering. Shallow Processing Techniques for NLP Ling570 November 30, 2011. Roadmap. Clustering Motivation & Applications Clustering Approaches Evaluation. Clustering. Task: Given a set of objects, create a set of clusters over those objects Applications:. Clustering.

1.09k views • 76 slides

Graph Clustering

Graph Clustering. Why graph clustering is useful?. Distance matrices are graphs  as useful as any other clustering Identification of communities in social networks Webpage clustering for better data management of web data. Outline. Min s-t cut problem Min cut problem Multiway cut

597 views • 44 slides

PARTITIONAL CLUSTERING

PARTITIONAL CLUSTERING. Deniz ÜSTÜN. CONTENT. WHAT IS CLUSTERING ? WHAT IS PARTITIONAL CLUSTERING ? THE USED ALGORITHMS IN PARTITIONAL CLUSTERING. What is Clustering ?.

656 views • 38 slides

Clustering Preliminaries

Clustering Preliminaries. Applications Euclidean/Non-Euclidean Spaces Distance Measures. The Problem of Clustering.

442 views • 30 slides

Clustering

Clustering. Types of Clustering. Objectives. At the end of this module the student will understand the following tasks and concepts. What clustering is and why you would want it Clustering options Differences between various types of clustering; advantages and disadvantages

643 views • 34 slides

K-means clustering

CZ5225: Modeling and Simulation in Biology Lecture 4: Clustering Analysis for Microarray Data II Prof. Chen Yu Zong Tel: 6874-6877 Email: yzchen@cz3.nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, NUS. K-means clustering. This method differs from the hierarchical clustering

1.65k views • 42 slides

Efficient Semi-supervised Spectral Co-clustering with Constraints

Efficient Semi-supervised Spectral Co-clustering with Constraints. Xiaoxiao Shi, Wei Fan, Philip S. Yu. Motivation. Co-clustering with constraints Document-word co-clustering. C. How to use?. Co-clustering. Network. Clustering. Doc 1 (ICNP). Doc 2 (ICDM). Doc 3 (AAAI). C. Doc 4 (KDD).

333 views • 13 slides

Spectral Clustering

Spectral Clustering. Jianping Fan Dept of Computer Science UNC, Charlotte. Lecture Outline. Motivation Graph overview and construction Spectral Clustering Cool implementations. Semantic interpretations of clustering clusters. Dataset exhibits complex cluster shapes

1.18k views • 80 slides

Bi-Clustering

Bi-Clustering. Jinze Liu. Outline. The Curse of Dimensionality Co-Clustering Partition-based hard clustering Subspace-Clustering Pattern-based. Clustering. K-means clustering minimizes. Where. The Curse of Dimensionality.

599 views • 33 slides

Bi-Clustering

Bi-Clustering. Data Mining: Clustering. K-means clustering minimizes. Where. Clustering by Pattern Similarity ( p- Clustering). The micro-array “raw” data shows 3 genes and their values in a multi-dimensional space Parallel Coordinates Plots Difficult to find their patterns

628 views • 24 slides

AGN Clustering

AGN Clustering. by Sean Lake for Astro 278 2012. Obligatory Outline Slide. Context: basic AGN review. How clustering studies can help. Clustering quantities: ξ(r), w p (r p ). What these quantities tell us. Clustering studies done. Conclusions. Basic Facts. Luminosity: ≥10 5 L ☉

260 views • 12 slides

Lecture 16: Clustering

Web Search and Mining. Lecture 16: Clustering. Introduction. Clustering. Document clustering Motivations Document representations Success criteria Clustering algorithms Flat Hierarchical. Introduction. What is clustering?.

682 views • 51 slides

Clustering

Clustering. Distance Measures Hierarchical Clustering k -Means Algorithms. The Problem of Clustering.

725 views • 54 slides

More Related

Measures of Clustering Quality: A Working Set of Axioms for Clustering

Measures of Clustering Quality: A Working Set of Axioms for Clustering

Presentation Transcript

Clustering

Clustering Preliminaries

Automatic Clustering &amp; Classification

Clustering II

Clustering III

Agenda

Collaborative Clustering for Entity Clustering

Lecture outline

Clustering

Graph Clustering

PARTITIONAL CLUSTERING

Clustering Preliminaries

Clustering

K-means clustering

Efficient Semi-supervised Spectral Co-clustering with Constraints

Spectral Clustering

Bi-Clustering

Bi-Clustering

AGN Clustering

Lecture 16: Clustering

Clustering

Automatic Clustering & Classification