CAS CS 565, Data Mining

CAS CS 565, Data Mining

Course logistics Course webpage: – http://www.cs.bu.edu/ evimaria/cs565-10.html Schedule: Mon – Wed, 4-5:30 Instructor: Evimaria Terzi, [email protected] Office hours: Mon 2:30-4pm, Tues 10:30am-12 (or by appointment) Mailing list : [email protected]

Topics to be covered (tentative) Introduction to data mining and prototype problems Frequent pattern mining – Frequent itemsets and association rules Clustering Dimensionality reduction Classification Link analysis ranking Recommendation systems Time-series data Privacy-preserving data mining

Syllabus Sept 8 Introduction to data mining Sept 13 Basic algorithms and prototype problems Sept 15, 20 Frequent itemsets and association rules Sept 22, 27, 29, Oct 4 Clustering algorithms Oct 6, 12 Dimensionality reduction Oct 11 Holiday-Tues instead Oct 13 Midterm exam Oct 18, 20, 25, 27 Classification Nov 1, 3, 8, 10 Link-analysis ranking Nov 15, 17, 22 Recommendation systems Nov 24, 29 Time series analysis Dec 6, 08 Privacy-preserving data mining Week starting Dec 13 Final exam; exact date to be determined

Course workload Three programming assignments (30%) Three problem sets (20%) Midterm exam (20%) Final exam (30%) Late assignment policy: 10% per day up to three days; credit will be not given after that Incompletes will not be given

Textbooks D. Hand, H. Mannila and P. Smyth: Principles of Data Mining. MIT Press, 2001 Jiawer Han and Micheline Kamber: Data Mining: Concepts and Techiques. Second Edition. Morgan Kaufmann Publishers, March 2006 Toby Segaran: Programming Collective Intelligence: Building Smart Web 2.0 Applications. O’Reilly Research papers (pointers will be provided)

Prerequisites Basic algorithms: sorting, set manipulation, hashing Analysis of algorithms: O-notation and its variants, perhaps some recursion equations, NP-hardness Programming: some programming language, ability to do small experiments reasonably quickly Probability: concepts of probability and conditional probability, expectations, binomial and other simple distributions Some linear algebra: e.g., eigenvector and eigenvalue computations

Above all The goal of the course is to learn and enjoy The basic principle is to ask questions when you don’t understand Say when things are unclear; not everything can be clear from the beginning Participate in the class as much as possible

Introduction to data mining Why do we need data analysis? What is data mining? Examples where data mining has been useful Data mining and other areas of computer science and statistics Some (basic) data-mining tasks

Why do we need data analysis Really really lots of raw data data!! – Moore’s law: more efficient processors, larger memories – Communications have improved too – Measurement technologies have improved dramatically – It possible to store and collect lots of raw data – The data-analysis methods are lagging behind Need to analyze the raw data to extract knowledge

The data is also very complex Multiple types of data: tables, time series, images, graphs, etc Spatial and temporal aspects Large number of different variables Lots of observations large datasets

Example: transaction data Billions of real-life customers: e.g., walmart, safeway customers, etc Billions of online customers: e.g., amazon, expedia, etc.

Example: document data Web as a document repository: billions of web pages Wikipedia: 4 million articles (and counting) Online collections of scientific articles

Example: network data Web: 50 billion pages linked via hyperlinks Facebook: 400 million users MySpace: 300 million users Instant messenger: 1billion users Blogs: 250 million blogs worldwide, presidential candidates run blogs

Example: genomic sequences http://www.1000genomes.org/page.php Full sequence of 1000 individuals 310 9 nucleotides per person 310 12 nucleotides Lots more data in fact: medical history of the persons, gene expression data

Example: environmental data Climate data (just an example) http://www.ncdc.gov/oa/climate/ghcn-monthly/index.php “a database of temperature, precipitation and pressure records managed by the National Climatic Data Center, Arizona State University and the Carbon Dioxide Information Analysis Center” “6000 temperature stations, 7500 precipitation stations, 2000 pressure stations”

We have large datasets so what? Goal: obtain useful knowledge from large masses of data “Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data analyst” Tell me something interesting about the data; describe the data Exploratory analysis on large datasets

What can data-mining methods do? Extract frequent patterns – There are lots of documents that contain the phrases “association rules”, “data mining” and “efficient algorithm” Extract association rules – 80% of the walmart customers that buy beer and sausage also buy mustard Extract rules – If occupation PhD student then income 20K

What can data-mining methods do? Rank web-query results – What are the most relevant web-pages to the query: “Student housing BU”? Find good recommendations for users – Recommend amazon customers new books – Recommend facebook users new friends/groups Find groups of entities that are similar (clustering) – Find groups of facebook users that have similar friends/interests – Find groups amazon users that buy similar products – Find groups of walmart customers that buy similar products

Goal of this course Describe some problems that can be solved using datamining methods Discuss the intuition behind data-mining methods that solve these problems Illustrate the theoretical underpinnings of these methods Show how these methods can be useful in practice

Data mining and related areas How does data mining relate to machine learning? How does data mining relate to statistics? Other related areas?

Data mining vs machine learning Machine learning methods are used for data mining – Classification, clustering Amount of data makes the difference – Data mining deals with much larger datasets and scalability becomes an issue Data mining has more modest goals – Automating tedious discovery tasks, not aiming at human performance in real discovery – Helping users, not replacing them

Data mining vs. statistics “tell me something interesting about this data” – what else is this than statistics? – The goal is similar – Different types of methods – In data mining one investigates lot of possible hypotheses – Data mining is more exploratory data analysis – In data mining there are much larger datasets algorithmics/scalability is an issue

Data mining and databases Ordinary database usage: deductive Knowledge discovery: inductive – Inductive reasoning is exploratory New requirements for database management systems Novel data structures, algorithms and architectures are needed

Data mining and algorithms Lots of nice connections A wealth of interesting research questions We will focus on some of these questions later in the course

Some simple data-analysis tasks Given a stream or set of numbers (identifiers, etc) How many numbers are there? How many distinct numbers are there? What are the most frequent numbers? How many numbers appear at least K times? How many numbers appear only once? etc

Finding the majority element A neat problem A stream of identifiers; one of them occurs more than 50% of the time How can you find it using no more than a few memory locations? Suggestions?

Finding the majority element (solution) A first item you see; count 1 for each subsequent item B if (A B) count count 1 else { count count - 1 if (count 0) {A B; count 1} } endfor return A Why does this work correctly?

Finding the majority element (solution and correctness proof) A first item you see; count 1 for each subsequent item B if (A B) count count 1 else { count count - 1 if (count 0) {A B; count 1} } endfor return A Basic observation: Whenever we discard element u we also discard a unique element v different from u

Finding a number in the top half Given a set of N numbers (N is very large) Find a number x such that x is *likely* to be larger than the median of the numbers Simple solution – Sort the numbers and store them in sorted array A – Any value larger than A[N/2] is a solution Other solutions?

Finding a number in the top half efficiently A solution that uses small number of operations – Randomly sample K numbers from the file – Output their maximum median N/2 items Failure probability (1/2) K N/2 items