'High dimensional data' presentation slideshows

Hong Cheng Jiawei Han

Integration of Classification and Pattern Mining: A Discriminative and Frequent Pattern-Based Approach. Hong Cheng Jiawei Han Chinese Univ. of Hong Kong Univ. of Illinois at U-C hcheng@se.cuhk.edu.hk hanj@cs.uiuc.edu

1.43k views • 126 slides

High Dimensional Data Analysis

The Sixth Sino-Japan-Korea Bioinformatics Training Course Yu Shyr ( ? ? ), Ph.D. March 30, 2007 Yu.Shyr@vanderbilt.edu. High Dimensional Data Analysis. Shyr, Yu ( ? ? ) , Ph.D. Ingram Distinguished Professor of Cancer Research Director

1.39k views • 99 slides

Introduction to Kernel Principal Component Analysis(PCA)

Introduction to Kernel Principal Component Analysis(PCA). Mohammed Nasser Dept. of Statistics, RU,Bangladesh Email: mnasser.ru@gmail.com. 1. Contents. Basics of PCA Application of PCA in Face Recognition Some Terms in PCA Motivation for KPCA Basics of KPCA Applications of KPCA.

1.18k views • 60 slides

An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data

An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data. Advisor : Dr. Hsu Presenter : Jing-Wei Lin. Outline. Motivation Objective Introduction SOM and AOI GSOM and EAOI Exploratory clustering and pattern extraction Experimental results Conclusions.

635 views • 53 slides

CIS664-Knowledge Discovery and Data Mining

CIS664-Knowledge Discovery and Data Mining. Clustering II. Vasileios Megalooikonomou Dept. of Computer and Information Sciences Temple University. (based on notes by Jiawei Han and Micheline Kamber). Agenda. What is Cluster Analysis? Types of Data in Cluster Analysis

646 views • 53 slides

Clustering IV

Clustering IV. Outline. Impossibility theorem for clustering Density-based clustering and subspace clustering Bi-clustering or co-clustering. General form of impossibility results. Define a set of simple axioms (properties) that a computational task should satisfy

763 views • 48 slides

The A-tree: An Index Structure for High-dimensional Spaces Using Relative Approximation

The A-tree: An Index Structure for High-dimensional Spaces Using Relative Approximation. Yasushi Sakurai (NTT Cyber Space Laboratories) Masatoshi Yoshikawa (Nara Institute of Science and Technology) Shunsuke Uemura (Nara Institute of Science and Technology)

592 views • 48 slides

Project Presentations

Project Presentations. Thursday this week, each student will make a 4-minute presentation on their project in class (with 1 or 2 minutes for questions) Email me your Powerpoint or PDF slides, with your name (e.g., joesmith.ppt), before 10am next Thursday Suggested content:

492 views • 37 slides

Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs

509 views • 37 slides

A Top-Down Row Enumeration Approach of Frequent Patterns from Very High Dimensional Data

A Top-Down Row Enumeration Approach of Frequent Patterns from Very High Dimensional Data. Jiaofen Xu. The Presentaion-Based Paper. Top-Down Mining of Frequent Patterns from Very High Dimensional Data. Hongyan Liu, Jiawei Han, Dong Xin and Zheng Shao. Outline. ?. Introduction

499 views • 33 slides

Outline

Outline. Regression on a large number of correlated inputs A few comments about shrinkage methods, such as ridge regression Methods using derived input directions Principal components regression Partial least squares regression (PLS). Partitioning of the expected squared prediction error.

470 views • 31 slides

Characteristics in Flight Data

Characteristics in Flight Data. Characteristics in Flight Data Estimation with Logistic Regression and Support Vector Machines. ICRAT 2004 Claus Gwiggner, LIX, Ecole Polytechnique Palaiseau Gert Lanckriet, EECS, University of California, Berkeley. Flow Management and Planning Differences.

381 views • 29 slides

418 views • 27 slides

Data Mining: Opportunities and Challenges

637 views • 24 slides

Projection Pursuit

513 views • 22 slides

Projection Pursuit

466 views • 22 slides

Chapter 6. Classification and Prediction

396 views • 14 slides

278 views • 14 slides

MicroArray Data Analysis Candice Quadros & Amol Kothari

MicroArray Data Analysis Candice Quadros & Amol Kothari. Neural Network for classification. Harnessing the power of a neural network for classifying samples. Neural Network for classification. Reduce the no. of genes :

257 views • 13 slides

References

References. Book References:

147 views • 5 slides