450-101 Management Information System450-101 Management Information System
Decision Support SystemDecision Support System
ผศ.ดร. วิ�ภาดา เวิทย์ ประสิ�ทธิ์�� Office :CS320, Computer Science BuildingEmail :[email protected] :http://staff.cs.psu.ac.th/wiphadaPhone :0-7428-8596
2450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Business Intelligence Applications
1
2
3
45
Data Warehouse
3450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Levels of Managerial Decision Making
4450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Structure
• Structured (operational)– The procedures to follow when decision
is needed can be specified in advance
• Unstructured (strategic)– It is not possible to specify in advance
most of the decision procedures to follow
• Semi-structured (tactical)– Decision procedures can be pre-specified,
but not enough to lead to the correct decision
5450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Information Quality
• Information products made more valuable by their attributes, characteristics, or qualities
– Information that is outdated, inaccurate, or hard to understand has much less value
• Information has three dimensions– Time
– Content
– Form
6450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Attributes of Information Quality
7450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Support in Business
• Companies are investing in data-driven decision support application frameworks to help them respond to– Changing market conditions
– Customer needs
• This is accomplished by several types of– Management information
– Decision support
– Other information systems
8450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
1 Management Information Systems
• The original type of information system that supported managerial decision making
– Produces information products that support many day-to-day decision-making needs
– Produces reports, display, and responses
– Satisfies needs of operational and tactical decision makers who face structured decisions
9450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
2 Decision Support Systems
Management Information Systems
Decision Support Systems
Decision support provided
Provide information about the performance of the
organization
Provide information and techniques to analyze
specific problems
Information form and frequency
Periodic, exception, demand, and push reports and
responses
Interactive inquiries and responses
Information format
Prespecified, fixed format Ad hoc, flexible, and adaptable format
Information processing methodology
Information produced by extraction and manipulation of
business data
Information produced by analytical modeling of
business data
10450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Support Systems
• Decision support systems use the following to support the making of semi-structured business decisions– Analytical models
– Specialized databases
– A decision-maker’s own insights and judgments
– An interactive, computer-based modeling process
• DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers
11450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
DSS Components
12450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Support Trends
• The emerging class of applications focuses on
– Personalized decision support
– Modeling
– Information retrieval
– Data warehousing
– What-if scenarios
– Reporting
13450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
DSS Model Base
• Model Base
– A software component that consists of models used in computational and analytical routines that mathematically express relations among variables
• Spreadsheet Examples
– Linear programming
– Multiple regression forecasting
– Capital budgeting present value
14450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Using Decision Support Systems
• Using a decision support system involves an interactive analytical modeling process
– Decision makers are not demanding pre-specified information
– They are exploring possible alternatives
• What-If Analysis
– Observing how changes to selected variables affect other variables
15450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Data Visualization Systems
• DVS
– Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps)
– Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form
16450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Analysis of Customer Demographics
17450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Data Warehouse
• คลั�งข้�อมูลั หมูายถึ�ง .... หลั�กการหร�อวิ�ธี�การ เพื่��อรวิมูระบบ สารสเทศเพื่��อ การประมูวิลัผลัรายการข้�อมูลัท��เก�ดข้�"น ในแต่'ลัะวิ�นแต่'ลัะสายงาน มูารวิมูเป(นหน'วิยเด�ยวิก�น
เพื่��อสน�บสน)นการต่�ดส�นใจให�มู�ประส�ทธี�ภาพื่มูากย��งข้�"น
• คลั�งข้�อมูลั หมูายถึ�ง.... ข้�อมูลัในแหลั'งข้�อมูลัหลัายๆแหลั'ง เพื่��อประกอบการต่�ดส�นใจให�มู�ประส�ทธี�ภาพื่มูากย��งข้�"น
• คลั�งข้�อมูลั ไมู'ใช่'ผลั�ต่ภ�ณฑ์1 หร�อระบบส2าเร3จรป
• คลั�งข้�อมูลั มู�ควิามูเป(นส'วินต่�วิข้องแต่'ลัะองค1กร (Organization Customized System)
18450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Multi-Tiered ArchitectureMulti-Tiered Architecture
DataWarehouse
ExtractTransformLoadRefresh
OLAP Engine
AnalysisQueryReportsData mining
Monitor&
IntegratorMetadata
Data Sources Front-End Tools
Serve
Data Marts
Operational DBs
other
sources
Data Storage
OLAP Server
19450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ค)ณลั�กษณะข้องคลั�งข้�อมูลั
1. Subject-Oriented 2. Integrated 3. Time-Variant4. Non-Volatile
20450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ค)ณลั�กษณะข้องคลั�งข้�อมูลั 1. Subject-Oriented
ข้�อมูลัถึกจ�ดกลั)'มูให�เหมูาะสมูก�บการส�บค�น จ�ดต่ามูประเด3นหลั�กข้ององค1กร เช่'น
ลักค�า ส�นค�า ยอดข้ายข้�อมูลัจะ....ไมู'ถึกจ�ดต่ามูหน�าท��การงาน....ข้องโปรแกรมูใด
โปรแกรมูหน��ง เช่'นการควิบค)มูคลั�งส�นค�า การออกใบก2าก�บภาษ�
2. Integrated จ�ดข้�อมูลัให�อย'ในรปแบบเด�ยวิก�น จากแหลั'งข้�อมูลัหลัายแหลั'ง
21450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ค)ณลั�กษณะข้องคลั�งข้�อมูลั
3. Time-Variantข้�อมูลัต่�องมู�ควิามูถึกต่�อง เพื่ราะเก3บไวิ�ใช่�นาน - 510 ป7
4. Non-Volatileการปร�บปร)งข้�อมูลัเป(นการเพื่��มูข้�อมูลัใหมู'เข้�าไปเร��อยๆ ไมู'ใช่'การ
แทนท��ข้�อมูลัเก'าข้�อมูลัในคลั�งข้�อมูลั....ไมู'จ2าเป(น...ต่�องท2าการ Normalize
เหมู�อนในฐานข้�อมูลั (Data based)
22450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ข้�อด�ข้องคลั�งข้�อมูลั
1. ให�ผลัต่อบแทนในการลังท)นสง 2. ได�เปร�ยบค'แข้'ง วิ�เคราะห1ข้�อมูลัเพื่��อก2าหนดเป(นแผนกลัย)ทธี1ได�ก'อนค'แข้'ง เช่'นพื่ฤต่�กรรมูผ�บร�โภค 3. เพื่��มูประส�ทธี�ภาพื่ในการต่�ดส�นใจ มู�ข้�อมูลัครบถึ�วินจากอด�ต่จนถึ�งป:จจ)บ�น
23450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ข้�อเส�ยข้องคลั�งข้�อมูลั
1. ข้�"นต่อนการกรองข้�อมูลัใช่�เวิลัานาน ต่�องอาศ�ยผ�ท��มู�ควิามูช่2านาญในการกรองข้�อมูลั
2. แนวิโน�มูในการกรองข้�อมูลัเพื่��มูมูากข้�"นเร��อยๆ เพื่��มูควิามูซั�บซั�อนให�กระบวินการท2างาน 3.ใช่�เวิลัานานในการพื่�ฒนาคลั�งข้�อมูลั4.ระบบคลั�งข้�อมูลัมู�ควิามูซั�บซั�อนสง
24450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
• Successful knowledge management
– Creates techniques, technologies, systems, and rewards for getting employees to share what they know
– Makes better use of accumulated workplace and enterprise knowledge
3 Knowledge Management
25450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Knowledge Management Techniques
26450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
• Knowledge management systems– A major strategic use of IT– Manages organizational learning and know-how– Helps knowledge workers create, organize, and
make available important knowledge– Makes this knowledge available wherever and
whenever it is needed
• Knowledge includes– Processes, procedures, patents, reference works,
formulas, best practices, forecasts, and fixes
Knowledge Management Systems (KMS)
Knowledge Management
การจั�ดการควิามร��
ควิามร��แบบชั�ดแจั�ง (Explicit Knowledge) 20%
ควิามร��โดย์นั�ย์/แบบซ่!อนัเร�นั (Tacit Knowledge) 80%
อธิ์�บาย์ได�แต่!ย์�งไม!ถู�กนั&าไปบ�นัท'กอธิ์�บาย์ได�แต่!ไม!อย์ากอธิ์�บาย์
อธิ์�บาย์ไม!ได�
29450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ลังมู�อปฏิ�บ�ต่�ใช่�ต่�วิอย'าง
ทร�พื่ย1ส�น
ส��อ/ประช่)มู
เกลั�ยวิควิามูร � เกลั�ยวิควิามูร � SECI Model SECI Model
S EI C
30450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
KnowledgeSharing (KS)
KnowledgeVision (KV)
KnowledgeAssets (KA)
•สิ!วินัหั�วิ สิ!วินัต่า•มองวิ!าก&าลั�งจัะไปทางไหันั•ต่�องต่อบได�วิ!า “ท&า KM ไปเพื่+,ออะไร”
• สิ!วินักลัางลั&าต่�วิ • สิ!วินัท-,เป.นั “หั�วิใจั” • ใหั�ควิามสิ&าค�ญก�บการ
แลักเปลั-,ย์นัเร-ย์นัร�� • ชั!วิย์เหัลั+อ เก+1อก�ลัซ่',ง
ก�นัแลัะก�นั (Share & Learn)
•สิ!วินัหัาง •สิร�างคลั�งควิามร��•เชั+,อมโย์งเคร+อข่!าย์ •ประย์3กต่ ใชั� ICT “สิะบ�ดหัาง” •สิร�างพื่ลั�งจัาก CoPs
TUNA Model(Thai –UNAids)
31450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
• การบร�หัารจั�ดการ• เพื่+,อใหั�.. “ ”คนั ท-,ต่�องการใชั�ควิามร��• ได�ร�บ..ควิามร�� ท-,ต่�องการใชั�• ในัเวิลัา..ท-,ต่�องการ• เพื่+,อใหั�บรรลั3เป4าหัมาย์การท&างานั
(Source: APQC)
การจั�ดการควิามร��Right Knowledge…. Right People… Right Time…
32450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
จ�งหวิ�ด อ&าเภอ
ต่&าบลั
การบร�การช่)มูช่น
33450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ค3ณเอ+1อ ค3ณเอ+1อ เป.นัผ��บร�หัารระด�บสิ�งท&าหันั�าท-,จั�ดการควิามร��ข่ององค กร
ค3ณอ&านัวิย์ ค3ณอ&านัวิย์ เชั+,อมโย์งคนั สิร�างควิามสิ�มพื่�นัธิ์ ต่!อก�นั
ค3ณก�จั ค3ณก�จั ผ��ท-,ร�บผ�ดชัอบต่ามหันั�าท-,ข่องต่นั ค3ณลั�ข่�ต่ ค3ณลั�ข่�ต่ ผ��ท&าหันั�าท-,จัดบ�นัท'ก สิก�ดองค
ควิามร�� ค3ณวิ�ศาสิต่ร ค3ณวิ�ศาสิต่ร ออกแบบระบบไอท-
ท-มงานัพื่�ฒนัาการจั�ดการควิามร��
34450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
35450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
36450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
4 Online Analytical Processing
• OLAP
– Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives
– Done interactively, in real time, with rapid response to queries
37450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Multidimensional Data
• Sales volume as a function of product, month, and region
Pro
duct
Regio
n
Month
Dimensions: Product, Location, Time
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Hierarchical summarization paths
38450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
A Sample Data Cube
Total annual salesof TV in U.S.A.Date
Produ
ct
Cou
ntr
y
sum
sum TV
VCRPC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada
Mexico
sum
Dimensions: Product,Date,Country
39450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Cuboids Corresponding to the Cube
all
product date country
product,date product,country date, country
product, date, country
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D(base) cuboid
40450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Browsing a Data Cube• Visualization• OLAP capabilities• Interactive
manipulation
41450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Online Analysis Processing (OLAP)• กระบวินการประมูวิลัผลัข้�อมูลัทางคอมูพื่�วิเต่อร1 ท��ช่'วิยให�วิ�เคราะห1ข้�อมูลั
ในมู�ต่�ต่'างๆ (Multidimensional Data Analysis)
• การด2าเน�นการก�บ OLAP
1. Roll up 2. Drill Down3. Slice4. Dice
42450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Typical OLAP (on-line analytical processing) Operations
• 1 Roll up (drill-up): summarize data
– by climbing up hierarchy or by dimension reduction
– มู�การรวิมูหร�อสร)ปค'า• 2 Drill down (roll down): reverse of roll-up
– from higher level summary to lower level summary or detailed data, or introducing new dimensions
– มู�การกระจายค'าในรายลัะเอ�ยดมูากข้�"น ต่ามูช่น�ดข้�อมูลั
43450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Fact Table
44450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Roll Up and Drill Down
45450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Typical OLAP Operations
3 Slice เลั�อกพื่�จารณา...ผลัลั�พื่ธี1...บางส'วินท��เราสนใจต่�ดค'าต่ามู Dimension
4 Diceเลั�อกพื่�จารณา...พื่ลั�ก Dimension... ให�ต่รงต่ามูควิามู
ต่�องการข้องผ�ใช่�เช่'น จากมู)มูมูอง Shop-Product-Type ไปเป(น
Date-Product-Type
46450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Dimension
47450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
5 Data Mining
• Provides decision support through knowledge discovery– Analyzes vast stores of historical business
data– Looks for patterns, trends, and correlations– Goal is to improve business performance
48450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Data Mining (เหมู�องข้�อมูลั)
• เหมู�องข้�อมูลั เป(นเคร��องมู�อท��ช่'วิยให�ผ�ใช่�เข้�าถึ�งข้�อมูลัได�โดยต่รงจากฐานข้�อมูลัข้นาดใหญ'
• เหมู�องข้�อมูลั เป(นเคร��องมู�อ แลัะ Application ท��สามูารถึแสดงผลัการวิ�เคราะห1ข้�อมูลัทางสถึ�ต่�ได�
• เหมู�องข้�อมูลั หมูายถึ�งการวิ�เคราะห1ข้�อมูลั เพื่��อแยกประเภท จ2าแนกรปแบบแลัะควิามูส�มูพื่�นธี1ข้องข้�อมูลัจากคลั�งข้�อมูลัหร�อฐานข้�อมูลัข้นาดใหญ' น2าสารสนเทศไปใช่�ในการต่�ดส�นใจธี)รก�จ
• ได�องค1ควิามูร �ใหมู' (Knowledge Discovery)
• อาจอย'ในรปแบบข้องกฎเกณฑ์1 (Rule)
49450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Data Mining Process
50450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ค)ณลั�กษณะข้องเหมู�องข้�อมูลั1. ช่�"แนวิทางการต่�ดส�นใจแลัะคาดการณ1ผลัลั�พื่ธี12. เพื่��มูควิามูเร3วิในการวิ�เคราะห1ข้�อมูลั จากฐานข้�อมูลัข้นาดใหญ'3. ค�นหาส'วินประกอบท��ซั'อนอย'ในเอกสาร รวิมูถึ�งควิามูส�มูพื่�นธี1
ระหวิ'างส'วินประกอบต่'างๆ4. จ�ดกลั)'มูเอกสารต่ามูห�วิข้�อต่'างๆต่ามูนโยบายบร�ษ�ท
51450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
เทคน�คการท2าเหมู�องข้�อมูลั
5.1. Classification
5.2. Clustering
5.3. Association
5.4. Visualization
52450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
เทคน�คการท2าเหมู�องข้�อมูลั5.1. Classification : เทคน�คในการจ2าแนกกลั)'มูข้�อมูลัด�วิย
ค)ณลั�กษณะต่'างๆท��ได�มู�การก2าหนดไวิ�แลั�วิสร�างแบบจ2าลัองเพื่��อการพื่ยากรณ1ค'าข้�อมูลั (Predictive
Model) ในอนาคต่ เร�ยกวิ'า ......Supervised Learning
มู� 2 รปแบบTree Induction
Neural Network
5.2. Clustering : เทคน�คในการจ2าแนกกลั)'มูข้�อมูลัใหมู'ท��มู�ลั�กษณะคลั�ายก�นไวิ�กลั)'มูเด�ยวิก�น โดยไมู'มู�การจ�ดกลั)'มูข้�อมูลัต่�วิอย'างไวิ�ลั'วิงหน�า เร�ยกวิ'า .......Unsupervised Learning
53450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
เทคน�คการท2าเหมู�องข้�อมูลั5.3. Association : เทคน�คในการค�นพื่บองค1ควิามูร �ใหมู'
ด�วิยการเช่��อมูโยงกลั)'มูข้องข้�อมูลัท��เก�ดข้�"นในเหต่)การณ1เด�ยวิก�นไวิ�ด�วิยก�น
5.4. Visualization :เทคน�คท��ใช่�ในการแสดงผลัในรปแบบกราฟิAกหร�อ ข้�อมูลัหลัายมู�ต่�
54450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
• Classification: – predicts categorical class labels– classifies data (constructs a model)
based on the training set and the values (class labels) in a classifying attribute and ....uses it in classifying new data
• Prediction: – models continuous-valued functions, i.e.,
predicts unknown or missing values
Classification vs. Prediction
55450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Classification Process
1. Model construction: 2. Model usage:
56450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Classification Process
1. Model construction: describing a set of predetermined classes• Each tuple/sample is assumed to belong to a
predefined class, as determined by the class label attribute
• The set of tuples used for model construction: training set
• The model is represented as classification rules, decision trees, or mathematical formulae
57450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
1. Model Construction
TrainingData
NAME RANK YEARS TENUREDMike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yesDave Assistant Prof 6 noAnne Associate Prof 3 no
ClassificationAlgorithms
IF rank = ‘professor’OR years > 6THEN tenured = ‘yes’
Classifier(Model)
58450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
2. Model usage: for classifying future or unknown objectsEstimate accuracy of the model
• The known label of test sample is compared with the classified result from the model
• Accuracy rate is the percentage of test set samples that are correctly classified by the model
• Test set is independent of training set
Classification Process
59450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
2. Use the Model in Prediction
Classifier
TestingData
NAME RANK YEARS TENUREDTom Assistant Prof 2 noMerlisa Associate Prof 7 noGeorge Professor 5 yesJoseph Assistant Prof 7 yes
Unseen Data
(Jeff, Professor, 4)
Tenured?
60450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
What Is Prediction?
• Prediction is similar to classification– 1. Construct a model
– 2. Use model to predict unknown value
• Major method for prediction is regression
– Linear and multiple regression
– Non-linear regression
• Prediction is different from classification– Classification refers to predict categorical class
label
– Prediction models continuous-valued functions
61450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Data Mining Process
1. Data Preparation
2. Evaluating Classification Methods
62450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
1. Data Preparation
• Data cleaning– Preprocess data in order to reduce noise and
handle missing values
• Relevance analysis (feature selection)– Remove the irrelevant or redundant attributes
• Data transformation– Generalize and/or normalize data
63450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
2. Evaluating Classification Methods• Predictive accuracy• Speed and scalability
– time to construct the model– time to use the model
• Robustness– handling noise and missing values
• Scalability– efficiency in disk-resident databases
• Interpretability: – understanding and insight proved by the model
• Goodness of rules– decision tree size– compactness of classification rules
64450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised vs. Unsupervised Learning
• Supervised learning (classification)– Supervision: The training data (observations,
measurements, etc.) are accompanied by labels indicating the class of the observations
– New data is classified based on the training set
• Unsupervised learning (clustering)– The class labels of training data is unknown
– Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
65450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised Learning
66450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Unsupervised Learning
67450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining Techniques
68450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Tree
69450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Decision Tree
• Decision tree – A flow-chart-like tree structure– Internal node denotes a test on an attribute– Branch represents an outcome of the test– Leaf nodes represent class labels or class
distribution• Use of decision tree: Classifying an unknown sample
– Test the attribute values of the sample against the decision tree
70450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Classification by Decision Tree• Decision tree generation consists of two
phases1. Tree construction
•At start, all the training examples are at the root
•Partition examples recursively based on selected attributes
2. Tree pruning•Identify and remove branches that reflect noise or outliers
71450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Training Dataset
age income student credit_rating buys_computer<=30 high no fair no<=30 high no excellent no30…40 high no fair yes>40 medium no fair yes>40 low yes fair yes>40 low yes excellent no31…40 low yes excellent yes<=30 medium no fair no<=30 low yes fair yes>40 medium yes fair yes<=30 medium yes excellent yes31…40 medium no excellent yes31…40 high yes fair yes>40 medium no excellent no
This follows an example from Quinlan’s ID3
72450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Output: A Decision Tree for “buys_computer”
age?
overcast
student? credit rating?
no yes fairexcellent
<=30 >40
no noyes yes
yes
30..40
73450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining Techniques
74450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining Techniques
75450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Supervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining TechniquesSupervised Data Mining Techniques
76450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
What Is Association Mining?
• Association rule mining:– Finding frequent patterns, associations,
correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories.
• Applications:– Basket data analysis, cross-marketing,
catalog design, loss-leader analysis, clustering, classification, etc.
77450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Market Basket Analysis
• One of the most common uses for data mining– Determines what products customers purchase
together with other products
• Results affect how companies– Market products
– Place merchandise in the store
– Lay out catalogs and order forms
– Determine what new products to offer
– Customize solicitation phone calls
78450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Association RulesAssociation RulesAssociation RulesAssociation Rules
79450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Generating Association RulesGenerating Association RulesConfidence and Support
Generating Association RulesGenerating Association RulesConfidence and Support
-Milk -Cheese
-Bread -Eggs
Possible associations include the following:
1. If customers purchase milk they also purchase bread.
2. If customers purchase bread they also purchase milk.
3. If customers purchase milk and eggs they also purchase cheese and bread.
4. If customers purchase milk, cheese, and eggs they also purchase bread.
80450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
81450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
82450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
83450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
Generating Association RulesGenerating Association RulesMining Association Rules: An Example
Here are three of several possible three-item set rules:
84450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
The K-Means AlgorithmThe K-Means AlgorithmThe K-Means AlgorithmThe K-Means Algorithm
85450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
The K-Means AlgorithmThe K-Means AlgorithmThe K-Means AlgorithmThe K-Means Algorithm
86450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
The K-Means AlgorithmThe K-Means AlgorithmGeneral Considerations
The K-Means AlgorithmThe K-Means AlgorithmGeneral Considerations
87450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
The K-Means AlgorithmThe K-Means AlgorithmGeneral Considerations
The K-Means AlgorithmThe K-Means AlgorithmGeneral Considerations
88450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Clustering TechniquesClustering TechniquesClustering TechniquesClustering Techniques
89450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Clustering TechniquesClustering TechniquesClustering TechniquesClustering Techniques
90450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ต่�วิอย'างการน2าเหมู�องข้�อมูลัมูาใช่�งาน1. การต่ลัาด
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91450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ประโยช่น1ข้องเหมู�องข้�อมูลั1. ค�นหาข้�อมูลัโดยอาศ�ยเทคโนโลัย�ข้องเหมู�องข้�อมูลั2. ใช่�สถึาป:ต่ยกรรมูแบบ Client/Server
3. ผ�ใช่�ระบบไมู'จ2าเป(นต่�องท�กษะในการเข้�ยนโปรแกรมู4. ผ�ใช่�ต่�องก2าหนดข้อบเข้ต่แลัะเปBาหมูายข้องระบบให�ช่�ดเจน เพื่��อ
ควิามูรวิดเร3วิแลัะถึกต่�องต่ามูควิามูต่�องการ5. การประมูวิลัผลัแบบข้นานจะช่'วิยเพื่��มูประส�ทธี�ภาพื่แลัะควิามูเร3วิ
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92450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Geographic Information Systems
• GIS
– DSS uses geographic databases to construct
and display maps and other graphic displays
– Supports decisions affecting the geographic distribution of people and other resources
– Often used with Global Positioning Systems (GPS) devices
93450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Dashboard Example
94450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Executive Information Systems
• EIS
– Combines many features of MIS and DSS
– Provide top executives with immediate and easy access to information
– Identify factors that are critical to accomplishing strategic objectives (critical success factors)
– So popular that it has been expanded to managers, analysis, and other knowledge workers
95450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Enterprise Information Portals
• An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies– Available to all intranet users and select
extranet users
– Provides access to a variety of internal and external business applications and services
– Typically tailored or personalized to the user or groups of users
– Often has a digital dashboard
– Also called enterprise knowledge portals
96450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Enterprise Information Portal Components
97450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Enterprise Knowledge Portal
98450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
ReferenceData Mining: Concepts and Techniques (Chapter 6 Slide for textbook), Jiawei Han and Micheline Kamber, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada
Data Mining A tutorial-Based Primer, Richard J. Roiger and Michael W. Geatz, Pearson Education Inc., 2003
James A. O’Brien and George M. Marakas, Management Information Systems, 8th edition, McGraw-Hill /Irwin, 2008
99450-101 Management Information System Assit. Prof. Dr. Wiphada Wettayaprasit
Q & A
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