Overview on Big Data and Data Mining

1. Data Mining & Big Data
Landscape
Thanh Luong
30th Aug 2016

2. Agenda
• Data Mining, Data Science
• Data Analytics in Business
• Big Data
• Data Analytics Techniques
oClassification (Decision tree, ANN, K-nearest neighbor, Bayesian)
oRegression
oCluster
oText Mining
oSVM
oMachine Learning

3. Data- Information – Knowledge - Wisdom

4. Data, Information and Knowledge
• Statistic
• Machine
Learning
• Data Mining
• Data Science

5. Data Science and Business

6. Data Implementation for Enterprise

7. Data Implementation for Enterprise

8. What is Big Data?

9. Data Analysis Process

10. Data types vs mining methods
• Data types and models
oFlat data tables
oRelational databases
oTemporal & spatial data
oTransactional databases
oMultimedia data
oGenome databases
oMaterials science data
oTextual data
oWeb data
oEtc.
• Mining tasks and method
oClassification / Prediction
Decision trees
Bayesian classification
Neural networks
Rule induction
Support vector machine (SVM)
Hidden Markov Model
Etc
oDescription
Association analysis
Clustering
Summarization
Etc.

11. Data types
• Symbolic
oIndexing
oBinary
oBoolean
oNominal
oOrdinal
• Numeric
oInteger
oContinuous
• Structured vs Unstructured data
• Semi-structured data
• Supervised vs Unsupervised data

12. Data mining techniques
• Supervised Learning
(predictive ability based on past
data)
oClassification Statistics
oDecision Trees
oRegression
oArtificial Neural Networks (ANN)
oClassification machine learning
• Unsupervised Learning
(Exploratory analysis to discover
patterns)
oClustering Analysis
oAssociation Rules

13. Study 1: Classification - Overview

14. Study 1: Classification with decision tree
Outlook Temp Humidity Windy Play
Sunny Hot Normal True ??

15. Study 1: Classification with decision tree

16. Study 1: Classification with decision tree

17. Study 2: Regression
• List all the variable available for making the model
• Establish a Dependent Variable (DV) of interest
• Examine visual (if possible) relationships between variables of interest
• Find a way to predict DV using other variables

18. Study 3: ANN

19. Study 3: ANN

20. Study 4: Cluster Analysis (K-Means)
• Market Segmentation
• Product Portfolio
• Text Mining

21. Study 5: Association Rule Mining
• Market Segmentation
• Product Portfolio
• Text Mining

22. THANK YOU