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Data Mining ????

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Title: Data Mining ( ) Author: myday Keywords: Data Mining, Description: Data Mining ( ) Last modified by: MY DAY – PowerPoint PPT presentation

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Title: Data Mining ????


1
Data Mining????
Tamkang University
Introduction to Data Mining(??????)
1022DM01 MI4 Wed, 6,7 (1310-1500) (B216)
Min-Yuh Day ??? Assistant Professor ?????? Dept.
of Information Management, Tamkang
University ???? ?????? http//mail.
tku.edu.tw/myday/ 2014-02-19
2
????102????2?????????Spring 2014 (2014.02 -
2014.06)
  • ???????? (Data Mining)
  • ??????? (Min-Yuh Day)
  • ???????P (TLMXB4P)
  • ?????? ??? 2 ?? (2 Credits, Elective)
  • ?????? 6,7 (Wed 1310-1500)
  • ????B216

3
????
  • ????????? (Data Mining) ???????????
  • ??????
  • ??????
  • ????
  • ?????
  • ????
  • ?????????
  • ??????
  • SAS??????????? (SAS EM)
  • ???????????

4
Course Introduction
  • This course introduces the fundamental concepts
    and applications technology of data mining.
  • Topics include
  • Introduction to Data Mining
  • Association Analysis
  • Classification and Prediction
  • Cluster Analysis
  • Text and Web Mining
  • Big Data Analytics
  • Data Mining Using SAS Enterprise Miner (SAS EM)
  • Case Study and Implementation of Data Mining

5
????(Objective)
  • ?????????????????
  • Understand and apply the fundamental concepts and
    technology of data mining

6
???? (Syllabus)
  • ?? (Week) ?? (Date) ?? (Subject/Topics)
  • 1 103/02/19 ?????? (Introduction to Data
    Mining)
  • 2 103/02/26 ???? (Association Analysis)
  • 3 103/03/05 ????? (Classification and
    Prediction)
  • 4 103/03/12 ???? (Cluster Analysis)
  • 5 103/03/19 ???????? (SAS EM ????)
    Case Study 1 (Cluster Analysis
    K-Means using SAS EM)
  • 6 103/03/26 ???????? (SAS EM ????)
    Case Study 2 (Association
    Analysis using SAS EM)
  • 7 103/04/02 ??????? (Off-campus study)
  • 8 103/04/09 ???????? (SAS EM ????????)
    Case Study 3 (Decision Tree,
    Model Evaluation using SAS EM)

7
???? (Syllabus)
  • ?? (Week) ?? (Date) ?? (Subject/Topics)
  • 9 103/04/16 ???? (Midterm Project
    Presentation)
  • 10 103/04/23 ????? (Midterm Exam)
  • 11 103/04/30 ???????? (SAS EM ??????????)
    Case Study 4
    (Regression Analysis,
    Artificial Neural Network using SAS EM)
  • 12 103/05/07 ????????? (Text and Web
    Mining)
  • 13 103/05/14 ?????? (Big Data Analytics)
  • 14 103/05/21 ???? (Final Project
    Presentation)
  • 15 103/05/28 ????? (Final Exam)

8
?????????
  • ????
  • ????????
  • ????
  • ??????????

9
????
  • ?? (Slides)
  • ????
  • Applied Analytics Using SAS Enterprise Mining,
    Jim Georges, Jeff Thompson and Chip Wells, 2010,
    SAS
  • Decision Support and Business Intelligence
    Systems, Ninth Edition, Efraim Turban, Ramesh
    Sharda, Dursun Delen, 2011, Pearson
  • ???????????,??,Efraim Turban ??,?????,2011,??

10
???????????
  • ????
  • 3?
  • ????????
  • ?????30
  • ?????30
  • ???(???????????) 40

11
Team Term Project
  • Term Project Topics
  • Data mining
  • Web mining
  • Business Intelligence
  • 3-4 ????
  • ????? 2014/03/05 (?) ???????
  • ?????????????

12
Data Mining at the Intersection of Many
Disciplines
Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
13
Knowledge Discovery (KDD) Process
Knowledge
Pattern Evaluation
Data mining core of knowledge discovery process
Data Mining
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases
Source Han Kamber (2006)
14
Data WarehouseData Mining and Business
Intelligence
Increasing potential to support business decisions
End User
Decision Making
Business Analyst
Data Presentation
Visualization Techniques
Data Mining
Data Analyst
Information Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
DBA
Data Sources
Paper, Files, Web documents, Scientific
experiments, Database Systems
Source Han Kamber (2006)
15
Business PressuresResponsesSupport Model
Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
16
A Taxonomy for Data Mining Tasks
Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
17
The Evolution of BI Capabilities
Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
18
A High-Level Architecture of BI
Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
19
Mining the Social Web Analyzing Data from
Facebook, Twitter, LinkedIn, and Other Social
Media Sites
Source http//www.amazon.com/Mining-Social-Web-An
alyzing-Facebook/dp/1449388345
20
Web Mining Success Stories
  • Amazon.com, Ask.com, Scholastic.com,
  • Website Optimization Ecosystem

Source Turban et al. (2011), Decision Support
and Business Intelligence Systems
21
Business Intelligence Trends
  1. Agile Information Management (IM)
  2. Cloud Business Intelligence (BI)
  3. Mobile Business Intelligence (BI)
  4. Analytics
  5. Big Data

Source http//www.businessspectator.com.au/articl
e/2013/1/22/technology/five-business-intelligence-
trends-2013
22
Business Intelligence Trends Computing and
Service
  • Cloud Computing and Service
  • Mobile Computing and Service
  • Social Computing and Service

23
Business Intelligence and Analytics
  • Business Intelligence 2.0 (BI 2.0)
  • Web Intelligence
  • Web Analytics
  • Web 2.0
  • Social Networking and Microblogging sites
  • Data Trends
  • Big Data
  • Platform Technology Trends
  • Cloud computing platform

Source Lim, E. P., Chen, H., Chen, G. (2013).
Business Intelligence and Analytics Research
Directions. ACM Transactions on Management
Information Systems (TMIS), 3(4), 17
24
Business Intelligence and Analytics Research
Directions
  • 1. Big Data Analytics
  • Data analytics using Hadoop / MapReduce framework
  • 2. Text Analytics
  • From Information Extraction to Question Answering
  • From Sentiment Analysis to Opinion Mining
  • 3. Network Analysis
  • Link mining
  • Community Detection
  • Social Recommendation

Source Lim, E. P., Chen, H., Chen, G. (2013).
Business Intelligence and Analytics Research
Directions. ACM Transactions on Management
Information Systems (TMIS), 3(4), 17
25
Source Davenport, T. H., Patil, D. J. (2012).
Data Scientist. Harvard business review
26
Big Data 2014
http//www.ettoday.net/events/bigdata2014/
27
Summary
  • This course introduces the fundamental concepts
    and applications technology of data mining.
  • Topics include
  • Introduction to Data Mining
  • Association Analysis
  • Classification and Prediction
  • Cluster Analysis
  • Text and Web Mining
  • Big Data Analytics
  • Data Mining Using SAS Enterprise Miner (SAS EM)
  • Case Study and Implementation of Data Mining

28
Contact Information
  • ??? ?? (Min-Yuh Day, Ph.D.)
  • ??????
  • ???? ??????
  • ??02-26215656 2347
  • ??02-26209737
  • ???i716 (??????)
  • ?? 25137 ?????????151?
  • Email myday_at_mail.tku.edu.tw
  • ??http//mail.tku.edu.tw/myday/
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