'Distance metric' presentation slideshows

Chapter 3: Data Mining and Data Visualization

Chapter 3: Data Mining and Data Visualization. Modern Data Warehousing, Mining, and Visualization: Core Concepts by George M. Marakas. BCIS 4660 Spring 2006. 3-1: A Picture is Worth a Thousand Words.

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Preference and Diversity-based Ranking in Network-Centric Information Management Systems

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Unsupervised Learning and Data Mining

Unsupervised Learning and Data Mining. Unsupervised Learning and Data Mining. Clustering. Supervised Learning. Decision trees Artificial neural nets K-nearest neighbor Support vectors Linear regression Logistic regression. Supervised Learning. F(x): true function (usually not known)

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Expression Profiling

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Clustering (slide from Han and Kamber)

Clustering (slide from Han and Kamber). Clustering of data is a method by which large sets of data is grouped into clusters of smaller sets of similar data. The example below demonstrates the clustering of balls of same colour. There are a total of 10 balls which are of three different

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Lab 3 DAVID, Clustering and Classification

Lab 3 DAVID, Clustering and Classification. Yang Li Lin Liu Feb 10 & Feb 11, 2014. DAVID (gene set analysis). http://david.abcc.ncifcrf.gov/summary.jsp Biological processes Molecular function Cellular component. Other gene set analysis tools.

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Clustering and Partitioning for Spatial and Temporal Data Mining

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Clustering

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Exploiting Page-Level Upper Bound (PLUB) for Multi-Type Nearest Neighbor (MTNN) Queries Xiaobin Ma Advisor: Shashi Shek

Exploiting Page-Level Upper Bound (PLUB) for Multi-Type Nearest Neighbor (MTNN) Queries Xiaobin Ma Advisor: Shashi Shekhar Dec, 2005 . Outline. Motivation Problem statement Related work and our contributions Proposed algorithm and cost model Experiment design and results

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Fast Pattern Matching in Non-Linear Tone-Mapped Images

Fast Pattern Matching in Non-Linear Tone-Mapped Images. Yacov Hel-Or The Interdisciplinary Center (IDC), Israel Visiting Scholar - Google Hagit Hel-Or and Eyal David U. of Haifa, Israel. Pattern Matching under Tone Mapping. A given pattern p is sought in an image.

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Emergent Low Dimensional Behavior in Large Systems of Coupled Phase Oscillators

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Clustering Methods

Clustering Methods. For the Study of Transcription Regulatory Networks. Mehak Aziz. Recap: What is Clustering?. Clustering is the classification of a set of data or objects into subsets (clusters) so that the data in each subset (ideally) share some common trait. . Applications.

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Birch: An efficient data clustering method for very large databases

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Microarray Gene Expression Database (MGED) Ontology Working Group

Microarray Gene Expression Database (MGED) Ontology Working Group. Chris Stoeckert Center for Bioinformatics University of Pennsylvania July 26, 2001. http://www.mged.org. MGED Steering Committee. MGED Working Groups. MIAME v1.0. MIAME section on sample source and treatment.

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CSCI 3202: Introduction to AI Decision Trees

CSCI 3202: Introduction to AI Decision Trees. Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell). Outline. Decision Tree Representations ID3 and C4.5 learning algorithms (Quinlan 1986) CART learning algorithm (Breiman et al. 1985) Entropy, Information Gain Overfitting.

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SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition

SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition. Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik. Multi-class Image Classification Caltech 101. Vanilla Approach. For each image, select interest points

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PSO and ASO Variants/Hybrids/Example Applications & Results

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Data Mining: Multivariate clustering & classification with R

Data Mining: Multivariate clustering & classification with R. Eric Feigelson Penn State. Summer School in Astrostatistics for Astronomers XII May 2016. Astronomical context. Astronomers have constructed classifications of celestial objects for centuries:

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K Nearest Neighbor Classification Methods

K Nearest Neighbor Classification Methods. Qiang Yang. Training Set. Used for prediction/classification Given input x, (e.g., <sunny, normal, ..?> #neighbors = K (e.g., k=3) Often a parameter to be determined The form of the distance function

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The Idea in a Nutshell

The Idea in a Nutshell . Classify a new item based on the classes of its k nearest neighbors. Sensitivity to k distance metric how the k points contribute to the decision . Computational Challenges. Distance metric needs to be tuned (e.g., parameter scaling).

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