Data Mining: A Short Survey
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This presentation provides an overview of data mining, including its definition, importance, challenges, and techniques. It begins by defining data mining as identifying patterns in large volumes of data. The presenter notes the importance of data mining is understanding large amounts of data for decision making. Some challenges of data mining include extracting, transforming, and loading data, as well as analyzing and presenting results. The presentation then covers specific techniques like association rule learning, classification, clustering, and sequence analysis and provides examples of each. It concludes by listing additional resources for learning more about data mining.
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
- 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
- 13. ASSOCIATION RULE LEARNING PURPOSE “A method for discovering interesting relations between variables in large databases.”
- 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
- 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!