# Chapter 3: Cluster Analysis

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Chapter 3: Cluster Analysis 3.1 Basic Cncepts f Clustering Cluster Analysis 3.1. Clustering Categries 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA 3..5 CLARANS 3.3 Hierarchical Methds 3.4 Density-based Methds 3.5 Clustering High-Dimensinal Data 3. Outlier Analysis

2 3.1.1 Cluster Analysis Unsupervised learning (i.e., Class label is unknwn) Grup data t frm new categries (i.e., clusters), e.g., cluster huses t find distributin patterns Principle: Maximizing intra-class similarity & minimizing interclass similarity Typical Applicatins WWW, Scial netwrks, Marketing, Bilgy, Library, etc.

3 3.1. Clustering Categries Partitining Methds Cnstruct k partitins f the data Hierarchical Methds Creates a hierarchical decmpsitin f the data Density-based Methds Grw a given cluster depending n its density (# data bjects) Grid-based Methds Quantize the bject space int a finite number f cells Mdel-based methds Hypthesize a mdel fr each cluster and find the best fit f the data t the given mdel Clustering high-dimensinal data Subspace clustering Cnstraint-based methds Used fr user-specific applicatins

4 Chapter 3: Cluster Analysis 3.1 Basic Cncepts f Clustering Cluster Analysis 3.1. Clustering Categries 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA 3..5 CLARANS 3.3 Hierarchical Methds 3.4 Density-based Methds 3.5 Clustering High-Dimensinal Data 3. Outlier Analysis

5 3..1 Partitining Methds: The Principle Given A data set f n bjects K the number f clusters t frm Organize the bjects int k partitins (k<=n) where each partitin represents a cluster The clusters are frmed t ptimize an bjective partitining criterin Objects within a cluster are similar Objects f different clusters are dissimilar

6 3.. K-Means Methd Chse 3 bjects (cluster centrids) Gal: create 3 clusters (partitins) Assign each bject t the clsest centrid t frm Clusters Update cluster centrids + + +

7 K-Means Methd Recmpute Clusters If Stable centrids, then stp + + +

8 K-Means Algrithm Input K: the number f clusters D: a data set cntaining n bjects Output: A set f k clusters Methd: (1) Arbitrary chse k bjects frm D as in initial cluster centers () Repeat (3) Reassign each bject t the mst similar cluster based n the mean value f the bjects in the cluster (4) Update the cluster means (5) Until n change

9 K-Means Prperties The algrithm attempts t determine k partitins that minimize the square-errr functin E k i 1 p C i ( p m i ) E: the sum f the squared errr fr all bjects in the data set P: the data pint in the space representing an bject m i : is the mean f cluster C i It wrks well when the clusters are cmpact cluds that are rather well separated frm ne anther

10 K-Means Prperties Advantages K-means is relatively scalable and efficient in prcessing large data sets The cmputatinal cmplexity f the algrithm is O(nkt) n: the ttal number f bjects k: the number f clusters t: the number f iteratins Nrmally: k<<n and t<<n Disadvantage Can be applied nly when the mean f a cluster is defined Users need t specify k K-means is nt suitable fr discvering clusters with nncnvex shapes r clusters f very different size It is sensitive t nise and utlier data pints (can influence the mean value)

11 Variatins f the K-Means Methd A few variants f the k-means which differ in Selectin f the initial k means Dissimilarity calculatins Strategies t calculate cluster means Handling categrical data: k-mdes (Huang 9) Replacing means f clusters with mdes Using new dissimilarity measures t deal with categrical bjects Using a frequency-based methd t update mdes f clusters A mixture f categrical and numerical data Nvember, 010 Data Mining: Cncepts and Techniques 11

12 3..3 K-Medids Methd Minimize the sensitivity f k-means t utliers Pick actual bjects t represent clusters instead f mean values Each remaining bject is clustered with the representative bject (Medid) t which is the mst similar The algrithm minimizes the sum f the dissimilarities between each bject and its crrespnding reference pint E k i 1 p C i p i E: the sum f abslute errr fr all bjects in the data set P: the data pint in the space representing an bject O i : is the representative bject f cluster C i

13 K-Medids Methd: The Idea Initial representatives are chsen randmly The iterative prcess f replacing representative bjects by n representative bjects cntinues as lng as the quality f the clustering is imprved Fr each representative Object O Fr each nn-representative bject R, swap O and R Chse the cnfiguratin with the lwest cst Cst functin is the difference in abslute errr-value if a current representative bject is replaced by a nn-representative bject

14 K-Medids Methd: Example Data Objects O 1 A 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O Gal: create tw clusters Chse randmly tw medids O = (3,4) O = (7,4)

15 K-Medids Methd: Example Data Objects A 1 O 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O cluster Cluster1 = {O 1, O, O 3, O 4 } 10 cluster Assign each bject t the clsest representative bject Using L1 Metric (Manhattan), we frm the fllwing clusters 7 9 Cluster = {O 5, O, O 7, O, O 9, O 10 }

16 K-Medids Methd: Example O 1 A 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O 10 7 Data Objects Cmpute the abslute errr criterin [fr the set f Medids (O,O)] p E k i C p i i cluster1 cluster

17 K-Medids Methd: Example Data Objects A 1 O 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O cluster The abslute errr criterin [fr the set f Medids (O,O)] 10 cluster E ( 3 4 4) (3 11 ) 7 9 0

18 K-Medids Methd: Example Data Objects A 1 O 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O Chse a randm bject O 7 Swap O and O7 3 cluster Cmpute the abslute errr criterin [fr the set f Medids (O,O7)] 10 cluster E ( 3 4 4) ( 1 3 3) 7 9

19 K-Medids Methd: Example Data Objects A 1 O 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O cluster Cmpute the cst functin Abslute errr [fr O,O 7 ] Abslute errr [O,O ] S 0 cluster S> 0 it is a bad idea t replace O by O 7

20 K-Medids Methd Data Objects A 1 O 1 A O 3 4 O 3 3 O O 5 O 4 O O 7 4 O 9 5 O cluster In this example, changing the medid f cluster did nt change the assignments f bjects t clusters. 10 cluster What are the pssible cases when we replace a medid by anther bject? 7 9

21 K-Medids Methd Cluster 1 Cluster A B B First case The assignment f P t A des nt change p Representative bject Randm Object Currently P assigned t A Cluster 1 Cluster A p B B Secnd case P is reassigned t A Representative bject Randm Object Currently P assigned t B

22 K-Medids Methd Cluster 1 Cluster A p B B Third case P is reassigned t the new B Representative bject Randm Object Currently P assigned t B Cluster 1 Cluster A Furth case p B B P is reassigned t B Representative bject Randm Object Currently P assigned t A

23 K-Medids Algrithm(PAM) PAM : Partitining Arund Medids Input K: the number f clusters D: a data set cntaining n bjects Output: A set f k clusters Methd: (1) Arbitrary chse k bjects frm D as representative bjects (seeds) () Repeat (3) Assign each remaining bject t the cluster with the nearest representative bject (4) Fr each representative bject O j (5) Randmly select a nn representative bject O randm () Cmpute the ttal cst S f swapping representative bject Oj with O randm (7) if S<0 then replace O j with O randm () Until n change

24 K-Medids Prperties(k-medids vs.k-means) The cmplexity f each iteratin is O(k(n-k) ) Fr large values f n and k, such cmputatin becmes very cstly Advantages K-Medids methd is mre rbust than k-means in the presence f nise and utliers Disadvantages K-Medids is mre cstly that the k-means methd Like k-means, k-medids requires the user t specify k It des nt scale well fr large data sets

25 3..4 CLARA CLARA (Clustering Large Applicatins) uses a sampling-based methd t deal with large data sets A randm sample shuld clsely represent the riginal data sample PAM The chsen medids will likely be similar t what wuld have been chsen frm the whle data set

26 CLARA Draw multiple samples f the data set Apply PAM t each sample Chse the best clustering Return the best clustering Clusters Clusters Clusters PAM PAM PAM sample 1 sample sample m

27 CLARA Prperties Cmplexity f each Iteratin is: O(ks + k(n-k)) s: the size f the sample k: number f clusters n: number f bjects PAM finds the best k medids amng a given data, and CLARA finds the best k medids amng the selected samples Prblems The best k medids may nt be selected during the sampling prcess, in this case, CLARA will never find the best clustering If the sampling is biased we cannt have a gd clustering Trade ff-f efficiency

28 3..5 CLARANS CLARANS (Clustering Large Applicatins based upn RANdmized Search ) was prpsed t imprve the quality and the scalability f CLARA It cmbines sampling techniques with PAM It des nt cnfine itself t any sample at a given time It draws a sample with sme randmness in each step f the search

29 CLARANS: The idea Clustering view Current medids medids Cst=10 Cst=5 Cst=1 Cst=0 Cst= Cst=3 Cst=5 Keep the current medids

30 CLARA CLARANS: The idea Draws a sample f ndes at the beginning f the search Neighbrs are frm the chsen sample Restricts the search t a specific area f the riginal data First step f the search Neighbrs are frm the chsen sample Current medids Sample medids secnd step f the search Neighbrs are frm the chsen sample

31 CLARANS: The idea CLARANS Des nt cnfine the search t a lcalized area Stps the search when a lcal minimum is fund Finds several lcal ptimums and utput the clustering with the best lcal ptimum First step f the search Draw a randm sample f neighbrs Current medids Original data medids secnd step f the search Draw a randm sample f neighbrs The number f neighbrs sampled frm the riginal data is specified by the user

32 CLARANS Prperties Advantages Experiments shw that CLARANS is mre effective than bth PAM and CLARA Handles utliers Disadvantages The cmputatinal cmplexity f CLARANS is O(n ), where n is the number f bjects The clustering quality depends n the sampling methd

33 Summary f Sectin 3. Partitining methds find sphere-shaped clusters K- mean is efficient fr large data sets but sensitive t utliers PAM uses centers f the clusters instead f means CLARA and CLARANS are used fr clustering large databases

### ECLT5810 E-Commerce Data Mining Techniques SAS Enterprise Miner Neural Network

Enterprise Miner Neural Netwrk 1 ECLT5810 E-Cmmerce Data Mining Techniques SAS Enterprise Miner Neural Netwrk A Neural Netwrk is a set f cnnected input/utput units where each cnnectin has a weight assciated

### Licensing Windows Server 2012 R2 for use with virtualization technologies

Vlume Licensing brief Licensing Windws Server 2012 R2 fr use with virtualizatin technlgies (VMware ESX/ESXi, Micrsft System Center 2012 R2 Virtual Machine Manager, and Parallels Virtuzz) Table f Cntents

### Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff

Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 Stats 202: Data Mining and Analysis Lester Mackey September 23, 2015 (Slide credits: Sergi Bacallad) 1 / 24 Annuncements

### DIRECT DATA EXPORT (DDE) USER GUIDE

2 ND ANNUAL PSUG-NJ CONFERNCE PSUG-NJ STUDENT MANAGEMENT SYSTEM DIRECT DATA EXPORT (DDE) USER GUIDE VERSION 7.6+ APRIL, 2013 FOR USE WITH POWERSCHOOL PREMIER VERSION 7.6+ Prepared by: 2 TABLE OF CONTENTS

### Disk Redundancy (RAID)

A Primer fr Business Dvana s Primers fr Business series are a set f shrt papers r guides intended fr business decisin makers, wh feel they are being bmbarded with terms and want t understand a cmplex tpic.

### Times Table Activities: Multiplication

Tny Attwd, 2012 Times Table Activities: Multiplicatin Times tables can be taught t many children simply as a cncept that is there with n explanatin as t hw r why it is there. And mst children will find

### Implementing ifolder Server in the DMZ with ifolder Data inside the Firewall

Implementing iflder Server in the DMZ with iflder Data inside the Firewall Nvell Cl Slutins AppNte www.nvell.cm/clslutins JULY 2004 OBJECTIVES The bjectives f this dcumentatin are as fllws: T cnfigure

### The ad hoc reporting feature provides a user the ability to generate reports on many of the data items contained in the categories.

11 This chapter includes infrmatin regarding custmized reprts that users can create using data entered int the CA prgram, including: Explanatin f Accessing List Screen Creating a New Ad Hc Reprt Running

### Licensing Windows Server 2012 for use with virtualization technologies

Vlume Licensing brief Licensing Windws Server 2012 fr use with virtualizatin technlgies (VMware ESX/ESXi, Micrsft System Center 2012 Virtual Machine Manager, and Parallels Virtuzz) Table f Cntents This

### Getting Started Guide

fr SQL Server www.lgbinder.cm Getting Started Guide Dcument versin 1 Cntents Installing LOGbinder fr SQL Server... 3 Step 1 Select Server and Check Requirements... 3 Select Server... 3 Sftware Requirements...

### SolarWinds Technical Reference

SlarWinds Technical Reference Using Orin Grups and Dependencies Intrductin The Need t Manage Netwrks t Meet Business Gals... 3 Orin Service Grups (Grups)... 3 Nested Grups... 4 Grup Status... 5 Dependencies...

### Why Can t Johnny Encrypt? A Usability Evaluation of PGP 5.0 Alma Whitten and J.D. Tygar

Class Ntes: February 2, 2006 Tpic: User Testing II Lecturer: Jeremy Hyland Scribe: Rachel Shipman Why Can t Jhnny Encrypt? A Usability Evaluatin f PGP 5.0 Alma Whitten and J.D. Tygar This article has three

### Some Statistical Procedures and Functions with Excel

Sme Statistical Prcedures and Functins with Excel Intrductry Nte: Micrsft s Excel spreadsheet prvides bth statistical prcedures and statistical functins. The prcedures are accessed by clicking n Tls in

### Writing a Compare/Contrast Essay

Writing a Cmpare/Cntrast Essay As always, the instructr and the assignment sheet prvide the definitive expectatins and requirements fr any essay. Here is sme general infrmatin abut the rganizatin fr this

### Exercise 6: Gene Ontology Analysis

Overview: Intrductin t Systems Bilgy Exercise 6: Gene Ontlgy Analysis Gene Ontlgy (GO) is a useful resurce in biinfrmatics and systems bilgy. GO defines a cntrlled vcabulary f terms in bilgical prcess,

### ISAM TO SQL MIGRATION IN SYSPRO

118 ISAM TO SQL MIGRATION IN SYSPRO This dcument is aimed at assisting yu in the migratin frm an ISAM data structure t an SQL database. This is nt a detailed technical dcument and assumes the reader has

### How to deploy IVE Active-Active and Active-Passive clusters

Hw t deply IVE Active-Active and Active-Passive clusters Overview Juniper Netscreen SA and SM series appliances supprt Active/Passive r Active/Active cnfiguratins acrss a LAN r a WAN t prvide high availability,

### Fund Accounting Class II

Fund Accunting Class II BS&A Fund Accunting Class II Cntents Gvernmental Financial Reprting Mdel - Minimum GAAP Reprting Requirements... 1 MD&A (Management's Discussin and Analysis)... 1 Basic Financial

### ICT Security: the real challenge is cyberdefence

Sicurezza Infrmatica: nulla è più difficile che difendere un sistema ICT Security: the real challenge is cyberdefence F.Baiardi Dipartiment di Infrmatica Università di Pisa Infrmally: Therem: Crashing

### Traffic monitoring on ProCurve switches with sflow and InMon Traffic Sentinel

An HP PrCurve Netwrking Applicatin Nte Traffic mnitring n PrCurve switches with sflw and InMn Traffic Sentinel Cntents 1. Intrductin... 3 2. Prerequisites... 3 3. Netwrk diagram... 3 4. sflw cnfiguratin

### How do I evaluate the quality of my wireless connection?

Hw d I evaluate the quality f my wireless cnnectin? Enterprise Cmputing & Service Management A number f factrs can affect the quality f wireless cnnectins at UCB. These include signal strength, pssible

### WINDOW REPLACEMENT Survey

WINDOW REPLACEMENT Prperty wners and develpers undertaking rehabilitatin prjects fr bth Tax Act Certificatin and Sectin 106 Cmpliance are encuraged t repair and retain existing histric windws. Hwever,

### TRAINING GUIDE. Crystal Reports for Work

TRAINING GUIDE Crystal Reprts fr Wrk Crystal Reprts fr Wrk Orders This guide ges ver particular steps and challenges in created reprts fr wrk rders. Mst f the fllwing items can be issues fund in creating

### MiaRec. Performance Monitoring. Revision 1.1 (2014-09-18)

Revisin 1.1 (2014-09-18) Table f Cntents 1 Purpse... 3 2 Hw it wrks... 3 3 A list f MiaRec perfrmance cunters... 4 3.1 Grup MiaRec Statistics... 4 3.2 Grup MiaRec Call Statistics Per-State... 5 3.3 Grup

### Determining Efficient Solutions to Multiple. Objective Linear Programming Problems

Applied Mathematical Sciences, Vl. 7, 2013, n. 26, 1275-1282 HIKARI Ltd, www.m-hikari.cm Determining Efficient Slutins t Multiple Objective Linear Prgramming Prblems P. Pandian and M. Jayalakshmi Department

### TaskCentre v4.5 Send Message (SMTP) Tool White Paper

TaskCentre v4.5 Send Message (SMTP) Tl White Paper Dcument Number: PD500-03-17-1_0-WP Orbis Sftware Limited 2010 Table f Cntents COPYRIGHT 1 TRADEMARKS 1 INTRODUCTION 2 Overview 2 FEATURES 2 GLOBAL CONFIGURATION

### IX- On Some Clustering Techniques for Information Retrieval. J. D. Broffitt, H. L. Morgan, and J. V. Soden

IX-1 IX- On Sme Clustering Techniques fr Infrmatin Retrieval J. D. Brffitt, H. L. Mrgan, and J. V. Sden Abstract Dcument clustering methds which have been prpsed by R. E. Bnner and J. J. Rcchi are cmpared.

### 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

### Wireless Light-Level Monitoring

Wireless Light-Level Mnitring ILT1000 ILT1000 Applicatin Nte Wireless Light-Level Mnitring 1 Wireless Light-Level Mnitring ILT1000 The affrdability, accessibility, and ease f use f wireless technlgy cmbined

### In this lab class we will approach the following topics:

Department f Cmputer Science and Engineering 2013/2014 Database Administratin and Tuning Lab 8 2nd semester In this lab class we will apprach the fllwing tpics: 1. Query Tuning 1. Rules f thumb fr query

### MPDS Configuration Sheet Windows 2000

MPDS Cnfiguratin Sheet Windws 2000 Cnnecting t the Internet via a Mbile Packet Data service terminal Setting up a Windws 2000 mdem device The PC cmmunicates with the MPDS terminal as if it were a mdem.

### Data mining methodology extracts hidden predictive information from large databases.

Data Mining Overview By: Dr. Michael Gilman, CEO, Data Mining Technlgies Inc. With the prliferatin f data warehuses, data mining tls are flding the market. Their bjective is t discver hidden gld in yur

### Probability Models. The of a chance process is the set of all possible outcomes.

Prbability Mdels In Sectin 5.1, we used simulatin t imitate chance behavir. Frtunately, we dn t have t always rely n simulatins t determine the prbability f a particular utcme. Descriptins f chance behavir

### Using PayPal Website Payments Pro UK with ProductCart

Using PayPal Website Payments Pr UK with PrductCart Overview... 2 Abut PayPal Website Payments Pr & Express Checkut... 2 What is Website Payments Pr?... 2 Website Payments Pr and Website Payments Standard...

### Telenors remissvar över PTS förslag till revidering av hybridmodellen för samtrafikkostnader inom mobilnät beräkning av kapitalkostnader (WACC)

PTS Thrbjörn Blmdahl (Endast via e-pst) thrbjrn.blmdahl@pts.se smp@pts.se Stckhlm 2008-01-16 Telenrs remissvar över PTS förslag till revidering av hybridmdellen för samtrafikkstnader inm mbilnät beräkning

### Welcome to CNIPS Training: CACFP Claim Entry

Welcme t CNIPS Training: CACFP Claim Entry General Cmments frm SCN CACFP claiming begins with submissin f the Octber claim due by Nvember 15, 2012. Timelines/Due Dates With CNIPS, SCN will cntinue t enfrce

### 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0

DRAFT! April 1, 009 Cambridge University Press Feedback welcme 349 16 Flat clustering CLUSTER Clustering algrithms grup a set f dcuments int subsets r clusters The algrithms gal is t create clusters that

### Software and Hardware Change Management Policy for CDes Computer Labs

Sftware and Hardware Change Management Plicy fr CDes Cmputer Labs Overview The cmputer labs in the Cllege f Design are clsely integrated with the academic needs f faculty and students. Cmputer lab resurces

### Welcome to Microsoft Access Basics Tutorial

Welcme t Micrsft Access Basics Tutrial After studying this tutrial yu will learn what Micrsft Access is and why yu might use it, sme imprtant Access terminlgy, and hw t create and manage tables within

### EASTERN ARIZONA COLLEGE Database Design and Development

EASTERN ARIZONA COLLEGE Database Design and Develpment Curse Design 2011-2012 Curse Infrmatin Divisin Business Curse Number CMP 280 Title Database Design and Develpment Credits 3 Develped by Sctt Russell/Revised

### Data Validation and Iteration

Financial Mdeling Data Validatin and Iteratin As analysts are bmbarded with a lt ff data, ne shuld be able t check data veracity. This is where the data validatin functins cme in handy. Few excel functins

### Budget Planning. Accessing Budget Planning Section. Select Click Here for Budget Planning button located close to the bottom of Program Review screen.

Budget Planning Accessing Budget Planning Sectin Select Click Here fr Budget Planning buttn lcated clse t the bttm f Prgram Review screen. Depending n what types f budgets yur prgram has, yu may r may

### Data Science. IRDS: Data Mining Process. The term data mining. The term data mining

Science IRDS: Mining Prcess Charles Suttn Universit f Edinburgh Our rking definitin science is the stud f the cmputatinal principles, methds, and sstems fr etracting knledge frm data. A relativel ne term.

### NAVIPLAN PREMIUM LEARNING GUIDE. Analyze, compare, and present insurance scenarios

NAVIPLAN PREMIUM LEARNING GUIDE Analyze, cmpare, and present insurance scenaris Cntents Analyze, cmpare, and present insurance scenaris 1 Learning bjectives 1 NaviPlan planning stages 1 Client case 2 Analyze

### CONTRIBUTION TO T1 STANDARDS PROJECT. On Shared Risk Link Groups for diversity and risk assessment Sudheer Dharanikota, Raj Jain Nayna Networks Inc.

Bulder, CO., March 26-28, 2001 /2001-098 CONTRIBUTION TO T1 STANDARDS PROJECT TITLE SOURCE PROJECT On Shared Risk Link Grups fr diversity and risk assessment Sudheer Dharanikta, Raj Jain Nayna Netwrks

### From Beginner To Winner

Frm Beginner T Winner Beginner T Winner: Racing enjys immense ppularity fr many reasns. Racing fans natinwide, and in many parts f the wrld, lve viewing the spectacle f ne f nature's mst efficient and

### Outpatient Therapy G-Code Edit Findings January 30, 2014. Mary Sue Gardner, RN/BSN Senior Nurse Analyst

Outpatient Therapy G-Cde Edit Findings January 30, 2014 Mary Sue Gardner, RN/BSN Senir Nurse Analyst Backgrund Sectin 3005(g) f the Middle Class Tax Relief and Jbs Creatin Act (MCTRJCA) amended Sectin

### Access EEC s Web Applications... 2 View Messages from EEC... 3 Sign In as a Returning User... 3

EEC Single Sign In (SSI) Applicatin The EEC Single Sign In (SSI) Single Sign In (SSI) is the secure, nline applicatin that cntrls access t all f the Department f Early Educatin and Care (EEC) web applicatins.

### Configuring BMC AREA LDAP Using AD domain credentials for the BMC Windows User Tool

Cnfiguring BMC AREA LDAP Using AD dmain credentials fr the BMC Windws User Tl Versin 1.0 Cnfiguring the BMC AREA LDAP Plugin fr Dmain Username and Passwrds Intrductin...3 LDAP Basics...4 What is LDAP and

### This report provides Members with an update on of the financial performance of the Corporation s managed IS service contract with Agilisys Ltd.

Cmmittee: Date(s): Infrmatin Systems Sub Cmmittee 11 th March 2015 Subject: Agilisys Managed Service Financial Reprt Reprt f: Chamberlain Summary Public Fr Infrmatin This reprt prvides Members with an

### Trends and Considerations in Currency Recycle Devices. What is a Currency Recycle Device? November 2003

Trends and Cnsideratins in Currency Recycle Devices Nvember 2003 This white paper prvides basic backgrund n currency recycle devices as cmpared t the cmbined features f a currency acceptr device and a

### QAD Operations BI Metrics Demonstration Guide. May 2015 BI 3.11

QAD Operatins BI Metrics Demnstratin Guide May 2015 BI 3.11 Overview This demnstratin fcuses n ne aspect f QAD Operatins Business Intelligence Metrics and shws hw this functinality supprts the visin f

### PBX Remote Line Extension using Mediatrix 4104 and 1204 June 22, 2011

PBX Remte Line Extensin using Mediatrix 4104 and 1204 June 22, 2011 Prprietary 2011 Media5 Crpratin Table f Cntents Intrductin... 3 Applicatin Scenari... 3 Running the Unit Manager Netwrk Sftware... 4

### Exercise 5 Server Configuration, Web and FTP Instructions and preparatory questions Administration of Computer Systems, Fall 2008

Exercise 5 Server Cnfiguratin, Web and FTP Instructins and preparatry questins Administratin f Cmputer Systems, Fall 2008 This dcument is available nline at: http://www.hh.se/te2003 Exercise 5 Server Cnfiguratin,

### WHITE PAPER. Vendor Managed Inventory (VMI) is Not Just for A Items

WHITE PAPER Vendr Managed Inventry (VMI) is Nt Just fr A Items Why it s Critical fr Plumbing Manufacturers t als Manage Whlesalers B & C Items Executive Summary Prven Results fr VMI-managed SKUs*: Stck-uts

### Getting Started Guide

fr SharePint www.lgbinder.cm Getting Started Guide Dcument versin 3 Cntents Installing LOGbinder fr SharePint... 3 Step 1 Select Server and Check Sftware Requirements... 3 Select Server... 3 Sftware Requirements...

### Health Care Solution

Management Summary & Technical Overview Versin 1 5405 Altn Parkway, 5-A #359 Irvine, CA 92604 (949) 733-8526 Cpyright The prgrams and cncepts mentined herein are prprietary t, and are nt t be reprduced,

RE/MAX f Western Canada LeadStreet Brker Guide Ver. 2.0 Revisin Histry Name Date Versin Descriptin Tamika Anglin 09/04/13 1.0 Initial Creatin Tamika Anglin 11/05/13 2.0 Inclusin f instructins n reprting

### Lesson Study Project in Mathematics, Fall 2008. University of Wisconsin Marathon County. Report

Lessn Study Prject in Mathematics, Fall 2008 University f Wiscnsin Marathn Cunty Reprt Date: December 14 2008 Students: MAT 110 (Cllege Algebra) students at UW-Marathn Cunty Team Members: Paul Martin Clare

### AMERITAS INFORMATION TECHNOLOGY DISASTER RECOVERY AND DATA CENTER STRATEGY

AMERITAS INFORMATION TECHNOLOGY DISASTER RECOVERY AND DATA CENTER STRATEGY O VERVIEW There are currently 3 primary Data Center lcatins (Lincln, Cincinnati, and Calvert) and 2 secndary (Fallbrk and Philadelphia).

### Research Project Management - Taking Control of your Doctorate. Dr Keith E. Fildes 17 November 2015

- Taking Cntrl f yur Dctrate Dr Keith E. Fildes 17 Nvember 2015 Sessin Outline Visualising yur dctrate t better understand it Planning research prjects Managing research prjects Clsing research prjects

### Blizzard Ball: Snowballs versus Avalanches

Blizzard Ball: Snwballs versus Avalanches Blizzard Ball is a fun, active way t review the key cncepts f and debt cvered in Katrina s Classrm Lessn 3, A Fresh Start. The activity allws players t strategize

### 1.3. The Mean Temperature Difference

1.3. The Mean Temperature Difference 1.3.1. The Lgarithmic Mean Temperature Difference 1. Basic Assumptins. In the previus sectin, we bserved that the design equatin culd be slved much easier if we culd

### ENERGY CALIBRATION IN DPPMCA AND XRS-FP REV A0 ENERGY CALIBRATION IN DPPMCA AND XRS-FP

ENERGY CALIBRATION IN DPPMCA AND XRS-FP Energy calibratin is very imprtant fr quantitative X-ray analysis. Even a small errr in the calibratin f the energy scale can have significant cnsequences: if the

### Group Term Life Insurance: Table I Straddle Testing and Imputed Income for Dependent Life Insurance

An American Benefits Cnsulting White Paper American Benefits Cnsulting, LLC 99 Park Ave, 25 th Flr New Yrk, NY 10016 212 716-3400 http://www.abcsys.cm Grup Term Life Insurance: Table I Straddle Testing

efusin Cst Centers, Partner Funding, VAT/GST and ERP Link Table f Cntents Cst Centers... 2 Admin Setup... 2 Cst Center Step in Create Prgram... 2 Allcatin Types... 3 Assciate Payments with Cst Centers...

### COUNTRY REPORT: Sweden

COUNTRY REPORT: Sweden Prepared by: SP Technical Research Institute f Sweden Versin 2.0 Date: April 19, 2011 Address: Bx 857, SE-501 15 Brås, Sweden Tel. : +46 10 516 56 62 Fax : +46 33 13 19 79 E-mail:

### Phi Kappa Sigma International Fraternity Insurance Billing Methodology

Phi Kappa Sigma Internatinal Fraternity Insurance Billing Methdlgy The Phi Kappa Sigma Internatinal Fraternity Executive Bard implres each chapter t thrughly review the attached methdlgy and plan nw t

### Design for securability Applying engineering principles to the design of security architectures

Design fr securability Applying engineering principles t the design f security architectures Amund Hunstad Phne number: + 46 13 37 81 18 Fax: + 46 13 37 85 50 Email: amund@fi.se Jnas Hallberg Phne number:

### Access to the Ashworth College Online Library service is free and provided upon enrollment. To access ProQuest:

PrQuest Accessing PrQuest Access t the Ashwrth Cllege Online Library service is free and prvided upn enrllment. T access PrQuest: 1. G t http://www.ashwrthcllege.edu/student/resurces/enterlibrary.html

### Document Management Versioning Strategy

1.0 Backgrund and Overview Dcument Management Versining Strategy Versining is an imprtant cmpnent f cntent creatin and management. Versin management is a key cmpnent f enterprise cntent management. The

### J A M S. Enterprise Resource Planning (ERP) System Selection Using a Novel Integrated Weight Decision Making Method. R. V. Rao 1* and B. K.

Internatinal Jurnal f Advanced Manufacturing Systems Vlume 2 G Number 1 G January-June 2011 G pp. 21-28 Internatinal Science Press I J A M S Enterprise Resurce Planning (ERP) System Selectin Using a Nvel

### Application Note: 202

Applicatin Nte: 202 MDK-ARM Cmpiler Optimizatins Getting the Best Optimized Cde fr yur Embedded Applicatin Abstract This dcument examines the ARM Cmpilatin Tls, as used inside the Keil MDK-ARM (Micrcntrller

### Retirement Planning Options Annuities

Retirement Planning Optins Annuities Everyne wants a glden retirement. But saving fr retirement is n easy task. The baby bmer generatin is graying. Mre and mre peple are appraching retirement age. With

Business Cntinuity Prcedures Business Impact Analysis (BIA) System Recvery Prcedures (SRP) System Business Cntinuity Classificatin Cre Infrastructure Criticality Levels Critical High Medium Lw Required

### In this chapter, you will learn to use net present value analysis in cost and price analysis.

9.0 - Chapter Intrductin In this chapter, yu will learn t use net present value analysis in cst and price analysis. Time Value f Mney. The time value f mney is prbably the single mst imprtant cncept in

### CSE 231 Fall 2015 Computer Project #4

CSE 231 Fall 2015 Cmputer Prject #4 Assignment Overview This assignment fcuses n the design, implementatin and testing f a Pythn prgram that uses character strings fr data decmpressin. It is wrth 45 pints

### A Novel Method of Spam Mail Detection using Text Based Clustering Approach

Internatinal Jurnal f Cmputer Applicatins (0975 8887) Vlume 5 N.4, August 2010 A Nvel Methd f Spam Mail Detectin using Text Based Clustering Apprach M. Basavaraju Research Schlar, Dept. f CSE, CIT, Anna

WHITEPAPER BackupAssist Versin 6 www.backupassist.cm 2 Cntents 1. Requirements... 3 1.1 Remte SQL backup requirements:... 3 2. Intrductin... 4 3. SQL backups within BackupAssist... 5 3.1 Backing up system

### Level 3 Small Business Local SEO Package

Level 3 Small Business Lcal SEO Package NetLcal SEO Methdlgy Keywrd Research and Plan First we start by identifying a list f the best keywrds ( mney keywrds ) fr yur campaign. Using this list we develp

### Guide to Developing an RFP Process and Evaluating Proposals (May 2010) Alternative Education Loans. Introduction. Review of Materials

Guide t Develping an RFP Prcess and Evaluating Prpsals (May 2010) Alternative Educatin Lans Intrductin In the early 1990s, pstsecndary institutins began t publish Preferred Lender Lists (PLL) fr the benefit

### Networking Best Practices

Netwrking Best Practices Use f a Lad Balancer With Hitachi Cntent Platfrm and Hitachi Cntent Platfrm Anywhere By Hitachi Data Systems August 2015 Cntents Executive Summary... 3 Intrductin... 4 Lad Balancer

### Reproduction is the process by which organisms make more of their own kind from one generation to the next.

Chapter 1: Bilgy and Yu Sectin 1: Themes f Bilgy Objectives: Relate the seven prperties f life t a living rganism. Describe seven themes that can help yu rganize what yu learn abut bilgy. Identify the

### Firewall/Proxy Server Settings to Access Hosted Environment. For Access Control Method (also known as access lists and usually used on routers)

Firewall/Prxy Server Settings t Access Hsted Envirnment Client firewall settings in mst cases depend n whether the firewall slutin uses a Stateful Inspectin prcess r ne that is cmmnly referred t as an

### Position Paper on In-Network Object Cloud Architecture and Design Goals. Interconnecting Smart Objects with Internet Workshop 25 th March 2011

Architecture and Design Gals Intercnnecting Smart Objects with Internet Wrkshp 25 th March 2011 Alex Galis Stuart Clayman University Cllege Lndn Department

### UNIT PLAN. Methods. Soccer Unit Plan 20 days, 40 minutes in length. For 7-12 graders. Name

UNIT PLAN Methds Sccer Unit Plan 20 days, 40 minutes in length Fr 7-12 graders Name TABLE OF CONTENTS I. Title Page II. Table f Cntents III. Blck Time Frame IV. Unit Objectives V. Task Analysis VI. Evaluative

### Automatic identification and traceability of items and people in modern hospitals. Expositive document.

Prjektafdelingen fr Det Nye Universitetshspital Hedeager 3 DK-8200 Århus N Tel. +45 8728 8850 Prjektafd@dnu.rm.dk www.dnu.rm.dk Date 13-01-2013 Esben Wlf Autmatic identificatin and traceability f items

### Treasury Gateway Getting Started Guide

Treasury Gateway Getting Started Guide Treasury Gateway is a premier single sign-n and security prtal which allws yu access t multiple services simultaneusly thrugh the same sessin, prvides cnvenient access

### Within the program, students combine two or more areas of study into one interdisciplinary program. Current program options include:

Liberal Studies 2010-11 Prgram Descriptin Eastern Oregn University s Liberal Studies prgram ffers students an pprtunity t devise a persnalized prgram f study in an interdisciplinary apprach relevant t

### MapReduce Laboratory

MapReduce Labratry In this labratry students will learn hw t use the Hadp client API by wrking n a series f exercises: The classic Wrd Cunt and variatins n the theme Design Pattern: Pair and Stripes Design

### Business Intelligence represents a fundamental shift in the purpose, objective and use of information

Overview f BI and rle f DW in BI Business Intelligence & Why is it ppular? Business Intelligence Steps Business Intelligence Cycle Example Scenaris State f Business Intelligence Business Intelligence Tls

### STIOffice Integration Installation, FAQ and Troubleshooting

STIOffice Integratin Installatin, FAQ and Trubleshting Installatin Steps G t the wrkstatin/server n which yu have the STIDistrict Net applicatin installed. On the STI Supprt page at http://supprt.sti-k12.cm/,

### NOVA COLLEGE-WIDE COURSE CONTENT SUMMARY ITE 170 - MULTIMEDIA SOFTWARE (3 CR.)

Revised 8/2012 NOVA COLLEGE-WIDE COURSE CONTENT SUMMARY ITE 170 - MULTIMEDIA SOFTWARE (3 CR.) Curse Descriptin Explres technical fundamentals f creating multimedia prjects with related hardware and sftware.

### NAVIPLAN PREMIUM LEARNING GUIDE. Existing insurance coverage

NAVIPLAN PREMIUM LEARNING GUIDE Existing insurance cverage Cntents Existing insurance cverage 1 Learning bjectives 1 NaviPlan planning stages 1 Client case 2 Enter yur clients existing life, disability,

### A. Name. B. School district and building(s) List all that apply. C. Current assignment. D. Teaching license/certificate number

Educatr Standards Bard T. MASTER TEACHER RENEWAL APPLICATION SECTION I: Candidate Infrmatin A. Name B. Schl district and building(s) List all that apply C. Current assignment D. Teaching license/certificate

### Integrate Marketing Automation, Lead Management and CRM

Clsing the Lp: Integrate Marketing Autmatin, Lead Management and CRM Circular thinking fr marketers 1 (866) 372-9431 www.clickpintsftware.cm Clsing the Lp: Integrate Marketing Autmatin, Lead Management

### UNCITRAL COLLOQIUM ON FINANCING INTELLECTUAL PROPERTY ASSETS. (by: Kiriakoula Hatzikiriakos, McMillan Binch Mendelsohn)

UNCITRAL COLLOQIUM ON FINANCING INTELLECTUAL PROPERTY ASSETS (by: Kiriakula Hatzikiriaks, McMillan Binch Mendelshn) The purpse f this paper is t highlight sme issues and recmmendatins t be cnsidered during

### ATL: Atlas Transformation Language. ATL Installation Guide

ATL: Atlas Transfrmatin Language ATL Installatin Guide - versin 0.1 - Nvember 2005 by ATLAS grup LINA & INRIA Nantes Cntent 1 Intrductin... 3 2 Installing ADT frm binaries... 3 2.1 Installing Eclipse and