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# 101_2009_0_b

Course: ICT ICT2621, Summer 2010

School: University of South Africa

Word Count: 6606

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ASK131-U/101/0/2009 IMPORTANT INFORMATION: READ NOW DEPARTMENT OF FINANCIAL ACCOUNTING ACCOUNTING SKILLS ASK131U Tutorial Letter 101/2009 SCHEME OF WORK, STUDY RESOURCES AND ASSIGNMENTS Contents 1 2 3 4 5 6 7 8 A word of welcome Purpose and outcomes of this module Communication with your lecturers Student support system Study material How the assignment system works How the examination system works Assignments...

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University of South Africa - ICT - ICT2621
CONTENTSSTUDY UNIT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 TOPIC Accounting as an information system The business organisation Financial reporting The accounting cycle Components of the financial statements The accounting equation Percentages, pricing and
University of South Africa - ICT - ICT2621
University of South Africa - ICT - ICT2621
Question 1 1.1) (effort) = c(size)k Where effort is calcualate in person-months. Since this is based on 152 hours per month, You need to know the hours worked that month. 1.2) 3 years = 36 months = 36 * 152 = 5472 person-months With one developer, the eff
University of South Africa - ICT - ICT2621
ASSIGNMENT 2 Question 1 The requirements analysis phase defines the business requirements for a new system. The logical design phase further documents business requirements using system models that illustrate data structures, business processes, data flow
University of South Africa - ICT - ICT2621
Question 11.1) 2 A=5 E=9 1 B=9 3 F = 12 6 8 J=4 H=5 4 G = 10 7 K=4 10 D=8 5 9 I=5C = 111.2) The critical path is C, G, H which takes 26 days (11 + 10 + 5) There are 5 total paths. The other possible paths are: - A, D, I which takes 18 days - B, E, I wh
University of South Africa - ICT - ICT2621
University of South Africa - ICT - ICT2621
University of South Africa - ICT - ICT2621
INF206D/101/2008INF206D System Analysis and Design Methods Tutorial Letter 101School of Computing2Notice to StudentsDue to regulatory requirements (imposed by the Department of National Education) the following would apply for this academic year (200
San Jose State - CS - 146
San Jose State - CS - 146
San Jose State - CS - 146
PythonESSENTIAL REFERENCE Fourth EditionFh LibfLBdffDevelopers LibraryESSENTIAL REFERENCES FOR PROGRAMMING PROFESSIONALSDevelopers Library books are designed to provide practicing programmers with unique, high-quality references and tutorials on
San Jose State - CS - 420
Introduction to Weka A Data Mining PackageClick to edit Master subtitle style4/8/10Downloadhttp:/www.cs.waikato.ac.nz/ml/weka/4/8/10Install4/8/10Start.4/8/10First InterfaceThere are different models. In this set of slides, we will go via Knowle
San Jose State - CS - 420
San Jose State - CS - 420
San Jose State - CS - 420
CSE572:DataMiningLecture 7: Model Overfitting1ClassificationErrorsqTraining errors (apparent errors) Errors committed on the training set Test errors Errors committed on the test set Generalization errors Expected error of a model over random selecti
San Jose State - CS - 420
INFS4203/INFS7203DataMiningLectureNote7 LectureNote7 AssociationRuleMiningByGabrielFung,PhD SchoolofInformationTechnologyandElectricalEngineering TheUniversityOfQueenslandIntroduction Assume we have a store and only sold five items. Transaction recor
San Jose State - CS - 420
INFS4203/INFS7203DataMiningLectureNote3 LectureNote3 EvaluationByGabrielFung,PhD SchoolofInformationTechnologyandElectricalEngineering TheUniversityOfQueenslandTesting After Learning and before using the model (operation), we need to test it first! W
San Jose State - CS - 420
CSE 572/CBS 572: Data MiningLectureNote2 ClassificationIntroduction&amp;DecisionTreeClick to edit Master subtitle styleByGabrielFung,PhDOutlineIntroduction Classification Process Classification Model:Decision Tree2P. 2IntroductionClick to edit Master
San Jose State - CS - 420
CSE572DataMiningHuanLiu,CSE,CIDSE,ASUhttp:/www.public.asu.edu/~huanliu/DM10S/cse572.html04/08/10CSE572:DataMiningbyHuan Liu1CSE572Contentsofbasicandadvancedtopics FormatAninteractiveandhandsoncourse withampleopportunitiestowork,create andshare Asse
San Jose State - CS - 420
CSE572:DataMiningLecture 16: Hierarchical Clustering1HierarchicalClusteringProduces a set of nested clusters organized as a hierarchical tree q Can be visualized as a dendrogramq A tree like diagram that records the sequences of merges or splits60
San Jose State - CS - 420
Chapter 5: Ensemble MethodsCSE572, Spring 2010 Click to edit Master subtitle style4/8/10CSE 572 Spring 2009Ensemble MethodsConstruct a set of classifiers from the training data Predict class label of previously unseen records by aggregating predictio
San Jose State - CS - 420
CSE572/CBS572:DataMiningLecture 17: Density-based ClusteringRead Section 8.41DensitybasedClusteringqLocates regions of high density that are separated from one another by regions of low densityHigh density regionsLow density background6 density-b
San Jose State - CS - 420
CSE572:DataMiningLecture 3: Data Preprocessing1DataQualityIssuesqNoise Outliers Missing values Duplicate dataA side story related to data mining Local preference can determine global patterns Thomas Schellings famous experiment on what can cause rac
San Jose State - CS - 420
CSE572:DataMiningLecture 3: Data PreprocessingRead Section 2.31DataQualityIssuesqNoise Outliers Missing values Duplicate dataqqq2NoiseqNoise refers to modification of original values Examples: distortion of a persons voice when talking on a
San Jose State - CS - 420
CSE572:DataMiningLecture 3: Data PreprocessingRead Section 2.31DataQualityIssuesqNoise Outliers Missing values Duplicate dataqqq2NoiseqNoise refers to modification of original values Examples: distortion of a persons voice when talking on a
San Jose State - CS - 420
Definitions Examples Principals Elements WEB 2.0 Web Crawling CSE 572: Data Mining Collective IntelligenceHuan Liu Click to edit Master subtitle styleSpring, 2010 Joint Work with Mohammad Ali AbbasiCollective IntelligenceHuan Liu1Definitions Examp
San Jose State - CS - 420
CSE572/CBS572:DataMiningLecture 18: Cluster Validation1ClusterValidityqFor supervised classification we have a variety of measures to evaluate how good our model is Accuracy, precision, recallqFor cluster analysis, the analogous question is how to
San Jose State - CS - 420
CSE572DataMiningLecture Notes for Chapter 8 Basic Cluster Analysis Introduction to Data Miningby Tan, Steinbach, KumarHuan Liu, Spring 2010WhatisClusterAnalysis?qFinding groups of objects such that the objects in a group will be similar (or related)
San Jose State - CS - 420
DataMining Clustering:AdvancedConcepts andAlgorithmsLecture Notes 18 Tan,Steinbach, Kumar Tan,Steinbach,Introduction to Data Mining4/18/20041HierarchicalClustering:RevisitedqCreates nested clusters Agglomerative clustering algorithms vary in terms
San Jose State - CS - 420
DataMining ClusterAnalysisLecture Notes for Chapter 17 Tan,Steinbach, KumarIntroduction to Data Mining4/18/20041DBSCANqDBSCAN is a density-based algorithm. Density = number of points within a specified radius (Eps) A point is a core point if it h
San Jose State - CS - 420
CSE572:DataMiningBayesian Classifiers1Review:ProbabilityA random variable is a quantity that depends on the outcome of a random experiment Can be discrete or continuous q Example: Discrete random variableqRandom experiment: Tossing a coin 4 times Ra
San Jose State - CS - 420
CSE572:DataMiningBayesian Classifiers1Review:ProbabilityA random variable is a quantity that depends on the outcome of a random experiment Can be discrete or continuous q Example: Discrete random variableqRandom experiment: Tossing a coin 4 times Ra
San Jose State - CS - 420
CSE572:DataMiningLecture 9: Bayesian Classifiers1Review:ProbabilityA random variable is a quantity that depends on the outcome of a random experiment Can be discrete or continuous q Example: Discrete random variableqRandom experiment: Tossing a coin
San Jose State - CS - 420
SupplementaryNoteFromtheBook:DataMining: ConceptsandTechniques JiaweiHanandMichelineKamberDataMining:ConceptsandTechniques 1ConstructFPtreefroma TransactionDBTID 100 200 300 400 500 Steps: 1. ScanDBonce,find frequent1itemset(single itempattern) 2. Ord
San Jose State - CS - 420
Given the following t ransactions: E:1B:1 B:1 C:2 B:1 C:1B:1 A:2C:1C:1Root E:1 C:1B:1 C:1 E:1 C:2 C:2 A:1 E:1 Root B:1E:1 Root B:1 E:1 E:1 B:1 E:1 E:1 A:2 Root B:1 Root A:3 C:1 A:1 Transacti on1 2 3 4 5 ACD BCEH ABCE BEI AGJ ItemsPlease sketch the FP Tr
San Jose State - CS - 420
CSE572:DataMiningLecture 12: Association Analysis1FactorsAffectingComplexityofAprioriqChoice of minimum support threshold lowering support threshold results in more frequent itemsets this may increase number of candidates and max length of frequent
San Jose State - CS - 420
CSE572:DataMiningLecture 11: Association Analysis1AssociationRuleMiningqGiven a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transactionMarket-Basket transactionsTID Item
San Jose State - CS - 420
CSE 572 Data Mining, Sprig 2010, Feb 8, 2010 Some sample projects 1. Using clustering technique to mine the nature clusters of Haiti datasetChoose a toolkit such as WEKA, matlab clustering toolbox, etc. to categorize based on the text in reports. Documen
San Jose State - CS - 420
CSE572 - Project #1 Spring 2010Reza Zafarani reza@asu.edu Due: Monday Mar 1, 2010Submit a one pager report briey describing the following,1Problem statementDescribe what you are planning to do in the project. Write a single paragraph about the proble
San Jose State - CS - 420
Using Physical Activity for User Behavior AnalysisGerald BieberFraunhofer-Institute for Computer Graphics J.-Jungius-Str. 11 D-18059 Rostock, Germany Tel: (+49) 381-4024-110Christian PeterFraunhofer-Institute for Computer Graphics J.-Jungius-Str. 11 D
San Jose State - CS - 420
Using a Low-Cost Electroencephalograph for Task Classification in HCI ResearchJohnny Chung Lee Carnegie Mellon University 5000 Forbes Ave, Pittsburgh, PA 15213 johnny@cs.cmu.eduABSTRACTDesney S. Tan Microsoft Research One Microsoft Way, Redmond, WA 980
San Jose State - CS - 420
The BodyMedia Platform: Continuous Body IntelligenceAstro TellerCEO, BodyMedia, Inc. th 4 Smithfield Street, 12 Floor Pittsburgh, PA 15222 1.412.288.9901John (Ivo) StivoricCTO, BodyMedia, Inc. th 4 Smithfield Street, 12 Floor Pittsburgh, PA 15222 1.41
San Jose State - CS - 420
Predicting User Action from Skin ConductanceLszl Laufer Budapest University of Technology and Economics Dept. of Ergonomics and Psychology 1111 Bp. Egry J.u.1.E.p laufer@erg.bme.huABSTRACTBottyn Nmeth Budapest University of Technology and Economics Dep
San Jose State - CS - 420
Paper Presentation Assignment CSE 572 Spring 2010 Description: Search for and select a contemporary research paper focused on, or strongly related to, data mining. We welcome your suggestions/recommendations. Everyone is expected to search for and read th
San Jose State - CS - 420
Modeling Parallel and Reactive Empathy in Virtual Agents: An Inductive ApproachScott W. McQuiggan1, Jennifer L. Robison1, Robert Phillips12, and James C. Lester11Department of Computer Science North Carolina State University Raleigh, NC 27695 USAAppli
San Jose State - CS - 420
Improving Classication Accuracy Using Automatically Extracted Training DataMicrosoft Research Mountain View, CA, USAAriel Fuxmanarielf@microsoft.com Rakesh Agrawal rakesha@microsoft.com ABSTRACTMicrosoft Research Mountain View, CA, USAankannan@micros
San Jose State - CS - 420
Identifying Domain Expertise of Developers from Source CodeRenuka SindhgattaIBM India Research Laboratory Embassy Golf Links Park Bangalore, India 91-80-51774584renuka.sr@in.ibm.comABSTRACTWe are interested in identifying the domain expertise of deve
San Jose State - CS - 420
CHI 2008 Proceedings Physiological Sensing for InputApril 5-10, 2008 Florence, ItalyDemonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer InterfacesT. Scott Saponas1, Desney S. Tan2, Dan Morris2, Ravin Balakrishnan31DUB
San Jose State - CS - 420
Data Visualisation and Data Mining Technology for Supporting Care for Older PeopleNubia M. Gil *, Nicolas A. Hine *, John L. Arnott *, Julienne Hanson #, Richard G. Curry @, Telmo Amaral * &amp; Dorota Osipovi #* School of Computing, Dundee University, Dund
San Jose State - CS - 420
Creativity Support in IT Research OrganizationPriyamvada TripathiSchool of Computing and Informatics Arizona State University Tempe, AZ 85281 +1 480 294 2628pia@asu.edu ABSTRACTAll domains of human activity and society require creativity. This dissert
San Jose State - CS - 420
Can Complex Network Metrics Predict the Behavior of NBA Teams?Pedro O.S. Vaz de MeloFederal University of Minas Gerais 31270-901, Belo Horizonte Minas Gerais, BrazilVirgilio A.F. AlmeidaFederal University of Minas Gerais 31270-901, Belo Horizonte Mina
San Jose State - CS - 420
School of Computing, Informatics, and Decision Systems EngineeringData Mining (CSE 572)Spring 2010Instructor: Dr. Huan LiuTA: Mohammad Ali AbbasiHomework Deadline How to submit# 3, Classification Feb 24, 2010 Hard copy in the Class; before the class
San Jose State - CS - 420
School of Computing, Informatics, and Decision Systems EngineeringData Mining (CSE 572)Spring 2010Instructor: Dr. Huan LiuTA: Mohammad Ali AbbasiHomework Deadline How to submit# 4, Association Rule Mining Monday, March 22, 2010 Hard copy in the Clas
San Jose State - CS - 420
School of Computing, Informatics, and Decision Systems EngineeringData Mining (CSE 572)Spring 2010Instructor: Dr. Huan LiuTA: Mohammad Ali AbbasiHomework Deadline How to submit# 5, Clustering Monday, April 5th, 2010 Hard copy in the Class; before th
San Jose State - CS - 420
School of Computing, Informatics, and Decision Systems EngineeringData Mining (CSE 572)Spring 2010Instructor: Dr. Huan LiuTA: Mohammad Ali AbbasiHomework Deadline# 3, Classification Feb 24, 2010HW3-1: Consider the training example shown in Table 1,
San Jose State - CS - 146
Chapter 7Network Flow7.5 Bipartite MatchingSlides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved.1MatchingMatching. Input: undirected graph G = (V, E). M &quot; E is a matching if each node appears in at most edge in M. Max ma
San Jose State - CS - 146
Chapte 7 rNe twork FlowS s by Ke Wayne. lide vin C opyright 2005 Pe arson-Addison We sley. All rights reserve d.1S t Rail Ne ovie twork, 1955Re re : On thehistory of thetransportation and m umflow proble s. fe nce axim m Ale xande S r chrijve in Math
San Jose State - CS - 146