Data Mining: A Short Survey

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Arvin Jenabi

Data Mining: What is this, its challenges and more.

Technology

DATA MININGA Short Journey
A PRESENTATION BY
ARVIN JENABI (@arvinje)

AGENDA
WHAT WE’RE GOING TO COVER
๏ Data… Huh?
‣ Importance, Usage and Applications
๏ Challenges
‣ Why is it so Hard?
๏ Finding Relationships
‣ Path to Victory
๏ The Future
‣ Resources to Learn More

DATA…?
THE DEFINITION
“A complex process to identify
patterns in large volumes of data.”

DATA…?
WHY IS IT IMPORTANT?
Large Volume of Data
“Big Data Revolution”
Decision Making Process
Competitive Business World

DATA…?
THE DIFFERENCE
DATA MINING
VS.
STATISTICAL ANALYSIS

DATA…?
DATA MINING VS. STATISTICAL ANALYSIS
Hypothesizing
Data Types
Data Validity
Creating Formulas
Results

DATA…?
EXAMPLES
Chain Stores
Hospitals and Pharmacies
NBA Basketball

CHALLENGES
STEP BY STEP
Extract, Transform, Load (ETL)
Store and Manage
Analyzing
Presentation

CHALLENGES
TECHNIQUES
Search Tools
Visualization
Decision Tree
Artiﬁcial Neural Networks
Genetic Algorithms

FINDING RELATIONSHIPS
THE PATH TO VICTORY
Association Rule Learning
Classiﬁcation
Clustering
Sequence Analysis

ASSOCIATION RULE LEARNING
PURPOSE
“A method for discovering interesting
relations between variables in large
databases.”

ASSOCIATION RULE LEARNING
APPLICATIONS
Supermarkets

ASSOCIATION RULE LEARNING
APPLICATIONS
S u p p o r t
&
Conﬁdence

ASSOCIATION RULE LEARNING
FEATURES
๏ Support
‣ How frequently a speciﬁc item set occurs
๏ Conﬁdence
‣ How frequently items in Y appear in transactions that
contain X

CLASSIFICATION
PURPOSE
“The process of learning a model that
describes different classes of data.”

CLASSIFICATION
FEATURES
๏ Predetermined Classes
‣ A supervised method
๏ Decision Tree
‣ The basis for Models
‣ A set of if/thens

CLUSTERING
PURPOSE
“A process of partitioning a set of data
into a set of meaningful sub-classes.”

CLUSTERING
FEATURES
๏ Automated Process
‣ A.K.A Unsupervised
‣ Unlike Classiﬁcation
๏ Similar values for attributes
‣ The basis for grouping
๏ Disjoint Groups

SEQUENCE ANALYSIS
PURPOSE
“A Method of data mining to anticipate
behavior patterns and trends.”

THE FUTURE
REFERENCES
๏ Data Mining—Why is it Important?
‣ http://observationbaltimore.com/blog/2011/09/data-mining—why-is-it-important/
๏ Data Mining: What is Data Mining?
‣ http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/
datamining.htm
๏ Data Mining: Deﬁnition
‣ http://searchsqlserver.techtarget.com/deﬁnition/data-mining
๏ Association rules: Deﬁnition
‣ http://searchbusinessanalytics.techtarget.com/deﬁnition/association-rules-in-data-mining
๏ Association Analysis: Basic Concepts and Algorithms
‣ https://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf
๏ Data Mining - Clustering
‣ http://www.cs.put.poznan.pl/jstefanowski/sed/DM-7clusteringnew.pdf
๏ Data Mining - Cluster Analysis
‣ http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm

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Data Mining: A Short Survey

1. DATA MININGA Short Journey A PRESENTATION BY ARVIN JENABI (@arvinje)

2. AGENDA WHAT WE’RE GOING TO COVER ๏ Data… Huh? ‣ Importance, Usage and Applications ๏ Challenges ‣ Why is it so Hard? ๏ Finding Relationships ‣ Path to Victory ๏ The Future ‣ Resources to Learn More

3. DATA…? THE DEFINITION “A complex process to identify patterns in large volumes of data.”

4. DATA…? WHY IS IT IMPORTANT? Large Volume of Data “Big Data Revolution” Decision Making Process Competitive Business World

5. DATA…? THE DIFFERENCE DATA MINING VS. STATISTICAL ANALYSIS

6. DATA…? DATA MINING VS. STATISTICAL ANALYSIS Hypothesizing Data Types Data Validity Creating Formulas Results

7. DATA…? EXAMPLES Chain Stores Hospitals and Pharmacies NBA Basketball

8. CHALLENGES STEP BY STEP Extract, Transform, Load (ETL) Store and Manage Analyzing Presentation

9. CHALLENGES TECHNIQUES Search Tools Visualization Decision Tree Artiﬁcial Neural Networks Genetic Algorithms

10. AGENDA WHAT WE’RE GOING TO COVER ๏ Data… Huh? ‣ Importance, Usage and Applications ๏ Challenges ‣ Why is it so Hard? ๏ Finding Relationships ‣ Path to Victory ๏ The Future ‣ Resources to Learn More

11. FINDING RELATIONSHIPS THE PATH TO VICTORY Association Rule Learning Classiﬁcation Clustering Sequence Analysis

12. ASSOCIATION RULE LEARNING

13. ASSOCIATION RULE LEARNING PURPOSE “A method for discovering interesting relations between variables in large databases.”

14. ASSOCIATION RULE LEARNING APPLICATIONS Supermarkets

15. ASSOCIATION RULE LEARNING APPLICATIONS S u p p o r t & Conﬁdence

16. ASSOCIATION RULE LEARNING FEATURES ๏ Support ‣ How frequently a speciﬁc item set occurs ๏ Conﬁdence ‣ How frequently items in Y appear in transactions that contain X

17. CLASSIFICATION

18. CLASSIFICATION PURPOSE “The process of learning a model that describes different classes of data.”

19. CLASSIFICATION FEATURES ๏ Predetermined Classes ‣ A supervised method ๏ Decision Tree ‣ The basis for Models ‣ A set of if/thens

20. CLUSTERING

21. CLUSTERING PURPOSE “A process of partitioning a set of data into a set of meaningful sub-classes.”

22. CLUSTERING FEATURES ๏ Automated Process ‣ A.K.A Unsupervised ‣ Unlike Classiﬁcation ๏ Similar values for attributes ‣ The basis for grouping ๏ Disjoint Groups

23. SEQUENCE ANALYSIS

24. SEQUENCE ANALYSIS PURPOSE “A Method of data mining to anticipate behavior patterns and trends.”

25. THE FUTURE REFERENCES ๏ Data Mining—Why is it Important? ‣ http://observationbaltimore.com/blog/2011/09/data-mining—why-is-it-important/ ๏ Data Mining: What is Data Mining? ‣ http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/ datamining.htm ๏ Data Mining: Definition ‣ http://searchsqlserver.techtarget.com/definition/data-mining ๏ Association rules: Definition ‣ http://searchbusinessanalytics.techtarget.com/definition/association-rules-in-data-mining ๏ Association Analysis: Basic Concepts and Algorithms ‣ https://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf ๏ Data Mining - Clustering ‣ http://www.cs.put.poznan.pl/jstefanowski/sed/DM-7clusteringnew.pdf ๏ Data Mining - Cluster Analysis ‣ http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm

26. THANK YOU!

Data Mining: A Short Survey

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