Clustering Components of PySAL
|
|
|
- Stella Moody
- 10 years ago
- Views:
Transcription
1 Clustering Components of PySAL Sergio Rey 1,2 Juan Carlos Duque 1 Luc Anselin 2,3 1 Regional Analysis Laboratory (REGAL) Department of Geography San Diego State University 2 Regional Economics Application Laboratory (REAL) University of Illinois Urbana Champaign 3 Department of Geography University of Illinois Urbana Champaign Regional Science Association International Las Vegas, Nevada November 10-12, 2005 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
2 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
3 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
4 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
5 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
6 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
7 PySAL Origins Collaborative Project OpenSpace (UIUC) STARS (SDSU) ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
8 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
9 Objectives Collaborative Project OpenSpace (UIUC) STARS (SDSU) ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
10 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
11 Contiguity Constrained Clustering Aggregation of N areas to M regions (M < N), such that: 1 Each area belong to only one region. 2 The areas assigned to a region must be geographically connected. N = 47 M = 6 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
12 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
13 Methods ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
14 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
15 Spatial Aggregation Module Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
16 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
17 Methods ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
18 Menu Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
19 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
20 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
21 K-means two stages ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
22 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
23 Including Centroids Coordinates ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
24 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
25 Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
26 AZP-tabu ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
27 Comparison K-means two stages = ARISeL = AZP-Tabu = Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
28 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
29 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
30 Continue... ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
31 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
32 ARISeL ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
33 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
34 Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
35 ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
36 Sergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
37 Outline 1 PySAL Origins Objectives 2 Clustering Components Definition Methods 3 Aggregation Models in PySAL Forward Planning Up to date... K-means two stages Including Centroids Coordinates AZP-tabu Automatic Regionalization with Initial Seeds Location: ARISeL Minimum Spanning Tree Integrating Local Indicators of Spatial Association (LISA) 4 Future Directions ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
38 Integrating Local Indicators of Spatial Association (LISA) LISA yl i = f (w i., y) (1) Integrating into regionalization algorithms Kmeans Select k significant LISAs to serve as initial seeds. Kmeans Extend attribute set to include local statistics Kmeans Combine: LISA seeds and LISA attributes ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
39 Future Directions Regionalization Extensions to what we have done here. PySAL Clustering Other aspects of Clustering in PySAL ergio Rey, Juan Carlos Duque, Luc Anselin (San Diego State Clustering University) in PySAL RSAI / 36
New Tools for Spatial Data Analysis in the Social Sciences
New Tools for Spatial Data Analysis in the Social Sciences Luc Anselin University of Illinois, Urbana-Champaign [email protected] edu Outline! Background! Visualizing Spatial and Space-Time Association!
Clustering UE 141 Spring 2013
Clustering UE 141 Spring 013 Jing Gao SUNY Buffalo 1 Definition of Clustering Finding groups of obects such that the obects in a group will be similar (or related) to one another and different from (or
Constrained Clustering of Territories in the Context of Car Insurance
Constrained Clustering of Territories in the Context of Car Insurance Samuel Perreault Jean-Philippe Le Cavalier Laval University July 2014 Perreault & Le Cavalier (ULaval) Constrained Clustering July
ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING)
ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING) Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Preliminaries Classification and Clustering Applications
3/17/2009. Knowledge Management BIKM eclassifier Integrated BIKM Tools
Paper by W. F. Cody J. T. Kreulen V. Krishna W. S. Spangler Presentation by Dylan Chi Discussion by Debojit Dhar THE INTEGRATION OF BUSINESS INTELLIGENCE AND KNOWLEDGE MANAGEMENT BUSINESS INTELLIGENCE
Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/2004 Hierarchical
Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 by Tan, Steinbach, Kumar 1 What is Cluster Analysis? Finding groups of objects such that the objects in a group will
Spatial Data Analysis Using GeoDa. Workshop Goals
Spatial Data Analysis Using GeoDa 9 Jan 2014 Frank Witmer Computing and Research Services Institute of Behavioral Science Workshop Goals Enable participants to find and retrieve geographic data pertinent
Alejandro Betancourt. Curriculum Vitae. Education. Awards. Experience. As researcher
Alejandro Betancourt Curriculum Vitae 2013 Pres. Education PhD in Engineering. Erasmus Mundus Fellow in Interactive and Cognitive Environments. University of Genova and Eindhoven University of Technology.
Spatial Analysis with GeoDa Spatial Autocorrelation
Spatial Analysis with GeoDa Spatial Autocorrelation 1. Background GeoDa is a trademark of Luc Anselin. GeoDa is a collection of software tools designed for exploratory spatial data analysis (ESDA) based
Crime Hotspots Analysis in South Korea: A User-Oriented Approach
, pp.81-85 http://dx.doi.org/10.14257/astl.2014.52.14 Crime Hotspots Analysis in South Korea: A User-Oriented Approach Aziz Nasridinov 1 and Young-Ho Park 2 * 1 School of Computer Engineering, Dongguk
Clustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca
Clustering Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?
GeoDa 0.9 User s Guide
GeoDa 0.9 User s Guide Luc Anselin Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign Urbana, IL 61801 http://sal.agecon.uiuc.edu/ and
Exploring Spatial Data with GeoDa TM : A Workbook
Exploring Spatial Data with GeoDa TM : A Workbook Luc Anselin Spatial Analysis Laboratory Department of Geography University of Illinois, Urbana-Champaign Urbana, IL 61801 http://sal.uiuc.edu/ Center for
Clustering Connectionist and Statistical Language Processing
Clustering Connectionist and Statistical Language Processing Frank Keller [email protected] Computerlinguistik Universität des Saarlandes Clustering p.1/21 Overview clustering vs. classification supervised
MultiExperiment Viewer Quickstart Guide
MultiExperiment Viewer Quickstart Guide Table of Contents: I. Preface - 2 II. Installing MeV - 2 III. Opening a Data Set - 2 IV. Filtering - 6 V. Clustering a. HCL - 8 b. K-means - 11 VI. Modules a. T-test
Territorial Analysis for Ratemaking. Philip Begher, Dario Biasini, Filip Branitchev, David Graham, Erik McCracken, Rachel Rogers and Alex Takacs
Territorial Analysis for Ratemaking by Philip Begher, Dario Biasini, Filip Branitchev, David Graham, Erik McCracken, Rachel Rogers and Alex Takacs Department of Statistics and Applied Probability University
Web Work Module 11.6. User s Guide
Web Work Module 11.6 User s Guide COPYRIGHT Copyright 2000 2005 MainSaver Software. All rights reserved. No part of this document may be copied or distributed, transmitted, transcribed, stored in a retrieval
Data Mining. Cluster Analysis: Advanced Concepts and Algorithms
Data Mining Cluster Analysis: Advanced Concepts and Algorithms Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 More Clustering Methods Prototype-based clustering Density-based clustering Graph-based
Improving Regional PCE Estimates Using Credit Card Transaction Data
Improving Regional PCE Estimates Using Credit Card Transaction Data Abe Dunn Ledia Guci Mahsa Gholizadeh Bryn Whitmire June 10 th 2016 Data and coverage Exploratory work with First Data/Palantir Aggregate
A Study of Web Log Analysis Using Clustering Techniques
A Study of Web Log Analysis Using Clustering Techniques Hemanshu Rana 1, Mayank Patel 2 Assistant Professor, Dept of CSE, M.G Institute of Technical Education, Gujarat India 1 Assistant Professor, Dept
Environmental Remote Sensing GEOG 2021
Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class
Applying Data Analysis to Big Data Benchmarks. Jazmine Olinger
Applying Data Analysis to Big Data Benchmarks Jazmine Olinger Abstract This paper describes finding accurate and fast ways to simulate Big Data benchmarks. Specifically, using the currently existing simulation
Graduated with honors (Magna Cum Laude). Grade average: 9.6 over 10.
Esteban Meneses 1942 S Orchard St. Apt A, Urbana, IL 61801 217-344-0218 ([email protected]) Personal Data Passport Number: 303610389 Country of Origin: Costa Rica Civil status: married. Academic
STOCK MARKET TRENDS USING CLUSTER ANALYSIS AND ARIMA MODEL
Stock Asian-African Market Trends Journal using of Economics Cluster Analysis and Econometrics, and ARIMA Model Vol. 13, No. 2, 2013: 303-308 303 STOCK MARKET TRENDS USING CLUSTER ANALYSIS AND ARIMA MODEL
Raster Operations. Local, Neighborhood, and Zonal Approaches. Rebecca McLain Geography 575 Fall 2009. Raster Operations Overview
Raster Operations Local, Neighborhood, and Zonal Approaches Rebecca McLain Geography 575 Fall 2009 Raster Operations Overview Local: Operations performed on a cell by cell basis Neighborhood: Operations
They can be obtained in HQJHQH format directly from the home page at: http://www.engene.cnb.uam.es/downloads/kobayashi.dat
HQJHQH70 *XLGHG7RXU This document contains a Guided Tour through the HQJHQH platform and it was created for training purposes with respect to the system options and analysis possibilities. It is not intended
Introduction to Clustering
Introduction to Clustering Yumi Kondo Student Seminar LSK301 Sep 25, 2010 Yumi Kondo (University of British Columbia) Introduction to Clustering Sep 25, 2010 1 / 36 Microarray Example N=65 P=1756 Yumi
Master s Programs Offering Dual/Joint Degrees Report for NADD October 2012
Master s Programs Offering Dual/Joint Degrees Report for NADD October 2012 Prepared by the Office of Social Work Education and Research The data in this report were taken from the 2011 Annual Survey of
Data Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
Chapter 7. Cluster Analysis
Chapter 7. Cluster Analysis. What is Cluster Analysis?. A Categorization of Major Clustering Methods. Partitioning Methods. Hierarchical Methods 5. Density-Based Methods 6. Grid-Based Methods 7. Model-Based
BIRCH: An Efficient Data Clustering Method For Very Large Databases
BIRCH: An Efficient Data Clustering Method For Very Large Databases Tian Zhang, Raghu Ramakrishnan, Miron Livny CPSC 504 Presenter: Discussion Leader: Sophia (Xueyao) Liang HelenJr, Birches. Online Image.
Citibank Switch Kit Forms
Some things in life are still easy. Like switching to Citibank. Switching banks doesn t have to be a hassle. You just need to know what steps to follow to move your accounts to your new bank. And that
Clustering. Data Mining. Abraham Otero. Data Mining. Agenda
Clustering 1/46 Agenda Introduction Distance K-nearest neighbors Hierarchical clustering Quick reference 2/46 1 Introduction It seems logical that in a new situation we should act in a similar way as in
Data Exploration and Preprocessing. Data Mining and Text Mining (UIC 583 @ Politecnico di Milano)
Data Exploration and Preprocessing Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
SPSS Tutorial. AEB 37 / AE 802 Marketing Research Methods Week 7
SPSS Tutorial AEB 37 / AE 802 Marketing Research Methods Week 7 Cluster analysis Lecture / Tutorial outline Cluster analysis Example of cluster analysis Work on the assignment Cluster Analysis It is a
STATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and
Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table
Parallel Programming Map-Reduce. Needless to Say, We Need Machine Learning for Big Data
Case Study 2: Document Retrieval Parallel Programming Map-Reduce Machine Learning/Statistics for Big Data CSE599C1/STAT592, University of Washington Carlos Guestrin January 31 st, 2013 Carlos Guestrin
Statistical Databases and Registers with some datamining
Unsupervised learning - Statistical Databases and Registers with some datamining a course in Survey Methodology and O cial Statistics Pages in the book: 501-528 Department of Statistics Stockholm University
Tuition and Fees. & Room and Board. Costs 2011-12
National and Regional Comparisons of Tuition and Fees & Room and Board Costs 2011-12 Table of Contents Table of Contents... 1 Comparator Institutions... 3 University of Wyoming Comparator Institutions...
Creating and Manipulating Spatial Weights
Creating and Manipulating Spatial Weights Spatial weights are essential for the computation of spatial autocorrelation statistics. In GeoDa, they are also used to implement Spatial Rate smoothing. Weights
Social Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
A New Method of Estimating Locality of Industry Cluster Regions Using Large-scale Business Transaction Data
347-Paper A New Method of Estimating Locality of Industry Cluster Regions Using Large-scale Business Transaction Data Yuki Akeyama and Yuki Akiyama and Ryosuke Shibasaki Abstract In an industry cluster
How To Get Into A University
USA Recognition Introduction University of Cambridge International Examinations (CIE), is the world s largest provider of international qualifications for secondary and pre-university education. CIE qualifications
How To Create A Large Enterprise Cloud Storage System From A Large Server (Cisco Mds 9000) Family 2 (Cio) 2 (Mds) 2) (Cisa) 2-Year-Old (Cica) 2.5
Cisco MDS 9000 Family Solution for Cloud Storage All enterprises are experiencing data growth. IDC reports that enterprise data stores will grow an average of 40 to 60 percent annually over the next 5
Curriculum Vita Fall 2015
Curriculum Vita Fall 2015 Instructor: JoHyun (Jo) Kim, Ph.D. - Assistant Professor Academic Department: Department of Educational Leadership University Address: Educational Leadership Young Education North
COST MODEL FOR THE FIXED TELECOMMUNICATIONS NETWORK - PORTUGAL
COST MODEL FOR THE FIXED TELECOMMUNICATIONS NETWORK Hybrid Cost Proxy Model - PORTUGAL 17 November 2000 INDEX 1. INTRODUCTION... 3 2. HYBRID COST PROXY MODEL... 6 2.1 MODULES OF THE HYBRID COST PROXY MODEL...
Cluster Analysis. Alison Merikangas Data Analysis Seminar 18 November 2009
Cluster Analysis Alison Merikangas Data Analysis Seminar 18 November 2009 Overview What is cluster analysis? Types of cluster Distance functions Clustering methods Agglomerative K-means Density-based Interpretation
K-means Clustering Technique on Search Engine Dataset using Data Mining Tool
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 6 (2013), pp. 505-510 International Research Publications House http://www. irphouse.com /ijict.htm K-means
Data Mining 資 料 探 勘. 分 群 分 析 (Cluster Analysis)
Data Mining 資 料 探 勘 Tamkang University 分 群 分 析 (Cluster Analysis) DM MI Wed,, (:- :) (B) Min-Yuh Day 戴 敏 育 Assistant Professor 專 任 助 理 教 授 Dept. of Information Management, Tamkang University 淡 江 大 學 資
An Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
K-Means Cluster Analysis. Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1
K-Means Cluster Analsis Chapter 3 PPDM Class Tan,Steinbach, Kumar Introduction to Data Mining 4/18/4 1 What is Cluster Analsis? Finding groups of objects such that the objects in a group will be similar
CLUSTER ANALYSIS FOR SEGMENTATION
CLUSTER ANALYSIS FOR SEGMENTATION Introduction We all understand that consumers are not all alike. This provides a challenge for the development and marketing of profitable products and services. Not every
Cluster Analysis: Advanced Concepts
Cluster Analysis: Advanced Concepts and dalgorithms Dr. Hui Xiong Rutgers University Introduction to Data Mining 08/06/2006 1 Introduction to Data Mining 08/06/2006 1 Outline Prototype-based Fuzzy c-means
Map-Reduce for Machine Learning on Multicore
Map-Reduce for Machine Learning on Multicore Chu, et al. Problem The world is going multicore New computers - dual core to 12+-core Shift to more concurrent programming paradigms and languages Erlang,
Updated CellTracker software manual
Updated CellTracker software manual Chengjin Du, Till Bretschneider The software is developed based on the former version of CellTracker (http://dbkgroup.org/celltracker/). All the menu and functions of
How To Teach A Spatial Analysis Course
Course Outline SO 8243 Spatial Analysis of Social Data Semester: TBA Dr. Frank M. Howell Office: 324-A Etheredge Hall Phone: 662.325.7872 Office Hours: TBA E-mail: [email protected] (other times by appointment
How To Teach Deaf And Mute Communication
CURRICULUM VITA Jill R. Andrus, M.S., CCC-SLP Utah State University Department of Communicative Disorders and Deaf Education 1000 Old Main Hill Logan, UT 84322-1000 [email protected] BACKGROUND Education
Data Warehouse Design
Data Warehouse Design Modern Principles and Methodologies Matteo Golfarelli Stefano Rizzi Translated by Claudio Pagliarani Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City
Cluster Analysis: Basic Concepts and Algorithms
Cluster Analsis: Basic Concepts and Algorithms What does it mean clustering? Applications Tpes of clustering K-means Intuition Algorithm Choosing initial centroids Bisecting K-means Post-processing Strengths
XIAOBAI (BOB) LI ACADEMIC EXPERIENCE RESEARCH HIGHLIGHTS TEACHING HIGHLIGHTS
XIAOBAI (BOB) LI Department of Operations & Information Systems Manning School of Business One University Ave., Lowell, MA 01854 Phone: 978-934-2707 Email: [email protected] ACADEMIC EXPERIENCE 2011-present
University Your selection: 169 universities
University Your selection: 169 universities Level of study: bachelor, master Regions: United States, compareuni T eaching & Learning Research Knowledge T ransf er International Orientation Regional Engagement
Spatial Analysis of Five Crime Statistics in Turkey
Spatial Analysis of Five Crime Statistics in Turkey Saffet ERDOĞAN, M. Ali DERELİ, Mustafa YALÇIN, Turkey Key words: Crime rates, geographical information systems, spatial analysis. SUMMARY In this study,
Building Data Cubes and Mining Them. Jelena Jovanovic Email: [email protected]
Building Data Cubes and Mining Them Jelena Jovanovic Email: [email protected] KDD Process KDD is an overall process of discovering useful knowledge from data. Data mining is a particular step in the
Configuration Management Training Foundation
Spring/Fall 2016 Training - Certification CM & DM "San Diego Harbor View in HDR - from the good seats" Photo by Michael Steighner MDSimages Configuration Training Foundation Spring/Fall 2016 U.S.A. City
Data Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining
Data Mining Clustering (2) Toon Calders Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Outline Partitional Clustering Distance-based K-means, K-medoids,
Geography. LOWER-DIVISION TRANSFER PATTERN California State University (CSU) Statewide Pattern
August 20, 2009 California State University (CSU) Statewide Pattern The Lower-Division Transfer Pattern (LDTP) consists of the CSU statewide pattern of coursework outlined below, plus campus-specific coursework,
FastStats & Dashboard Product Overview
FastStats & Dashboard Product Overview Guide for Clients July 2011 Version 1 Matrix FastStats Overview Matrix believes that FastStats is an ideal analytics tool for UK Mortgage lenders. Matrix FastStats
Add-on MODULES FOR THE STONE PROFIT SYSTEM
Add-on S FOR THE STONE PROFIT SYSTEM What is it? CUSTOMER CONSIGNMENT Manages Inventory given to CONSIGNMENT CUSTOMER on consignment. Inventory is still an asset of YOUR COMPANY until consumed by the Consignment
INTEGRATING GIS AND SPATIAL DATA MINING TECHNIQUE FOR TARGET MARKETING OF UNIVERSITY COURSES
ISPRS SIPT IGU UCI CIG ACSG Table of contents Table des matières Authors index Index des auteurs Search Recherches Exit Sortir INTEGRATING GIS AND SPATIAL DATA MINING TECHNIQUE FOR TARGET MARKETING OF
Design & Analysis of Ecological Data. Landscape of Statistical Methods...
Design & Analysis of Ecological Data Landscape of Statistical Methods: Part 3 Topics: 1. Multivariate statistics 2. Finding groups - cluster analysis 3. Testing/describing group differences 4. Unconstratined
Tracking System for GPS Devices and Mining of Spatial Data
Tracking System for GPS Devices and Mining of Spatial Data AIDA ALISPAHIC, DZENANA DONKO Department for Computer Science and Informatics Faculty of Electrical Engineering, University of Sarajevo Zmaja
RAVEN: A GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework
INL/CON-13-28360 PREPRINT RAVEN: A GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework ANS Annual Meeting C. Rabiti D. Mandelli A. Alfonsi J. J. Cogliati R. Kinoshita D. Gaston R. Martineau
DR. YUEXIAO DONG. Department of Statistics Fox School of Business Temple University 1810 N. 13th Street Philadelphia, PA 19122
Department of Statistics Fox School of Business Temple University 1810 N. 13th Street Philadelphia, PA 19122 Phone: (1-)215-204-0670 Email: [email protected] http://astro.temple.edu/ ydong/index.html Employment
MASTER OF SOFTWARE ENGINEERING DEGREE TRACKS 1. WEB DEVELOPMENT/JAVA:
MASTER OF SOFTWARE ENGINEERING DEGREE TRACKS Students seeking a professional focus in the Master of Software Engineering program may choose to follow a specific set of courses referred to as a track, that
Corporate Clients. Client List, Feb 2014. Aerotek. Bellagio. Booz Allen Hamilton. FirstPic Inc. FranklinCovey. Genesis - United Kingdom
Client List, Feb 2014 Corporate Clients Aerotek Bellagio Booz Allen Hamilton FirstPic Inc FranklinCovey Genesis - United Kingdom Life Skills Center for Leadership Mercyhurst College Civic Institute MGM
The THREDDS Data Repository: for Long Term Data Storage and Access
8B.7 The THREDDS Data Repository: for Long Term Data Storage and Access Anne Wilson, Thomas Baltzer, John Caron Unidata Program Center, UCAR, Boulder, CO 1 INTRODUCTION In order to better manage ever increasing
Comparative Analysis of EM Clustering Algorithm and Density Based Clustering Algorithm Using WEKA tool.
International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 9, Issue 8 (January 2014), PP. 19-24 Comparative Analysis of EM Clustering Algorithm
GeoDa: An Introduction to Spatial Data Analysis
Geographical Analysis ISSN 0016-7363 GeoDa: An Introduction to Spatial Data Analysis Luc Anselin 1, Ibnu Syabri 2, Youngihn Kho 1 1 Spatial Analysis Laboratory, Department of Geography, University of Illinois,
IB 411 Bioinspiration - Syllabus Fall 2015
IB 411 Bioinspiration - Syllabus Fall 2015 Course Description This fully online, 8- week course (using a Moodle LMS) focuses on how experts in biology and technological fields find inspiration in nature
Fluency With Information Technology CSE100/IMT100
Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999
Metamodeling by using Multiple Regression Integrated K-Means Clustering Algorithm
Metamodeling by using Multiple Regression Integrated K-Means Clustering Algorithm Emre Irfanoglu, Ilker Akgun, Murat M. Gunal Institute of Naval Science and Engineering Turkish Naval Academy Tuzla, Istanbul,
Clustering census data: comparing the performance of self-organising maps and k-means algorithms
Clustering census data: comparing the performance of self-organising maps and k-means algorithms Fernando Bação 1, Victor Lobo 1,2, Marco Painho 1 1 ISEGI/UNL, Campus de Campolide, 1070-312 LISBOA, Portugal