1. Data Mining http://www.mmds.org Modified from
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Stanford University

3. Data contains value and knowledge
Knowledge discovered from data can be used for competitive advantage
Data contains value and knowledge

4. Data Mining ≈ Big Data ≈ Predictive Analytics ≈ Data Science
also called Knowledge Discovery and Data mining (KDD)
But to extract the knowledge data needs to be
Stored
Managed
And ANALYZED
Data Mining ≈ Big Data ≈ Predictive Analytics ≈ Data Science
Data Mining: This term refers to applying the powerful tools of computer science to solve problems in science, industry, and many other application areas.
Frequently, the key to a successful application is building a model of the data, that is, a summary or relatively succinct representation of the most relevant features of the data.

5. What is Data Mining? Given lots of data
Data mining is extraction of useful
patterns from data sources or
models of data
Discover patterns and models that are:
Valid: hold on new data with some certainty
Useful: should be possible to act on the item
Unexpected: non-obvious to the system
Understandable: humans should be able to interpret the pattern

6. Why is data mining important?
CS583, Bing Liu, UIC
Why is data mining important?
Computerization of businesses produce huge amount of data
How to make best use of data?
Knowledge discovered from data can be used for competitive advantage
Online businesses generate even larger data sets
Online retailers (e.g., amazon.com) are largely driving by data mining
Web search engines are information retrieval (text mining) and data mining companies
Web Data Mining

7. Why is data mining necessary?
Make use of your data assets
There is a big gap from stored data to knowledge;
and the transition won’t occur automatically.
Many interesting things that one wants to find cannot be found using database queries
“find people likely to buy my products”
“Who are likely to respond to my promotion”
“Which movies should be recommended to each customer?”
Web Data Mining

8. Why data mining? The data is abundant (big data)
CS583, Bing Liu, UIC
Why data mining?
The data is abundant (big data)
The computing power is not an issue
Data mining tools are available
The competitive pressure is very strong
Almost every company is doing (or has to do) it
Web Data Mining

9. Data Mining Tasks Descriptive methods Predictive methods
Find human-interpretable patterns that describe the data
Example: Classification, Clustering
Predictive methods
Use some variables to predict unknown or future values of other variables
Example: Recommender systems

10. Data Mining Models and Tasks
Vijay Raghavan, University of Louisiana Lafayette

11. Classic data mining tasks
CS583, Bing Liu, UIC
Classic data mining tasks
Classification:
mining patterns that can classify future (new) data into known classes
supervised learning
Association rule mining
sets of data items that occur together frequently
any rule of the form X  Y, where X and Y are sets of data items
Cheese, Milk Bread [sup =5%, confid=80%]
Clustering
identifying a set of similarity groups in the data
unsupervised learning
Web Data Mining

12. Classic data mining tasks (contd)
Sequential pattern mining:
sets of data items that occur together frequently in some sequences
A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence
Deviation detection:
discovering the most significant changes in data
Data visualization:
using graphical methods to show patterns in data.
Web Data Mining

13. Meaningfulness of Analytic Answers
A risk with “Data mining” is that an analyst can “discover” patterns that are meaningless
Statisticians call it Bonferroni’s principle:
Roughly, if you look in more places for interesting patterns than your amount of data will support, you are bound to find bogus
Called “data dredging” in statistics
Bonferroni’s Principle: If we are willing to view as an interesting feature of data something of which many instances can be expected to exist in random data, then we cannot rely on such features being significant. This observation limits our ability to mine data for features that are not sufficiently rare in practice.

14. Meaningfulness of Analytic Answers
Example:
We want to find (unrelated) people who at least twice have stayed at the same hotel on the same day
109 people being tracked
1,000 days
Each person stays in a hotel 1% of time (1 day out of 100)
Hotels hold 100 people (so 105 hotels)
If everyone behaves randomly (i.e., no terrorists) will the data mining detect anything suspicious?
Expected number of “suspicious” pairs of people:
250,000
… too many combinations to check – we need to have some additional evidence to find “suspicious” pairs of people in some more efficient way

15. What matters when dealing with data?
Challenges
Usage
Quality
Context
Streaming
Scalability
Collect
Prepare
Ontologies
Text
Networks
Signals
Data
Modalities
Represent
Structured
Multimedia
Model
Reason
Visualize
Data Operators

16. Data Mining: Cultures Data mining overlaps with: Different cultures:
Databases: Large-scale data, simple queries
Machine learning: Small data, Complex models
CS Theory: (Randomized) Algorithms
Different cultures:
To a DB person, data mining is an extreme form of analytic processing – queries that examine large amounts of data
Result is the query answer
To a ML person, data-mining is the inference of models
Result is the parameters of the model
CS Theory
Machine Learning
Data Mining
Database systems

17. Data Mining: Cultures
Data Mining overlaps with machine learning, statistics, artificial intelligence, databases but more stress on
Scalability (big data)
Algorithms
Computing architectures
Automation for handling large data
Statistics
Machine
Learning
Data Mining
Database systems

18. Data Mining: Related Areas
Database
Management
Systems
Other Areas:
Neural Networks
Evolutionary Methods
Information Retrieval
Etc.
The ‘Parent’
of Data Mining
Machine
Learning
Statistics
Pattern
Recognition
Visualization
Artificial
Intelligence
Expert
Systems
Data
Mining
Sometimes considered
an subarea of AI
Sometimes considered
an subarea of AI
Vijay Raghavan, University of Louisiana Lafayette

19. Data Mining vs. Databases
Data mining is the process of extracting hidden and actionable patterns from data
Database systems store and manage data
Queries return part of stored data
Queries do not extract hidden patterns
Examples of querying databases
Find all employees with income more than $250K
Find top spending customers in last month
Find all students from engineering college with GPA more than average

20. Knowledge Discovery and Data mining (KDD) process
Understand the application domain
Identify data sources and select target data
Pre-processing:
cleaning, attribute selection, etc
Data mining to extract patterns or models
Post-processing:
identifying interesting or useful patterns/knowledge
Incorporate patterns/knowledge in real world tasks
Web Data Mining

21. Knowledge Discovery and Data mining (KDD) process
Note, order of initial steps
and what is included in
each step may vary depending
on who you talk to
Patterns must be:
valid, novel, potentially useful, understandable
Vijay Raghavan, University of Louisiana Lafayette

22. How It All Fits Together
Locality sensitive hashing
Clustering
Dimensionality reduction
High dim. data
PageRank, SimRank
Community Detection
Spam Detection
Graph data
Filtering data streams
Web advertising
Queries on streams
Infinite data
Support vector machines
Decision Trees
Perceptron, kNN
Machine learning
Recommender systems
Association Rules
Duplicate document detection
Apps

23. Examples of Data Mining Applications
Fraud/Spam Detections: Identifying fraudulent transactions of a credit card or spam s
You are given a user’s purchase history and a new transaction, identify whether the transaction is fraud or not;
Determine whether a given is spam or not
Frequent Patterns: Extracting purchase patterns from existing records
beer ⇒ dippers (80%)
Forecasting: Forecasting future sales and needs according to some given samples
Finding Like-Minded Individuals: Extracting groups of like-minded people in a given network
Beer and chips are not interesting patterns, for example.

24. How do you want that data?
I ♥
data
How do you want that data?