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CSE591 (575) Data Mining

1/21/2003 - 5/6/2003Computer Science &

EngineeringASU

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Introduction

Introduction to this CourseIntroduction to Data Mining

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Introduction to the Course First, about you - why take this course?

Your background and strength AI, DBMS, Statistics, Biology, …

Your interests and requests What is this course about?

Problem solving Handling data

transform data to workable data Mining data

turn data to knowledge validation and presentation of knowledge

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This course What can you expect from this course?

Knowledge and experience about DM Problem solving and solution presentation

How is this course conducted? Presentations Individual projects

Course Format Individual Projects 40% Exams and/or quizzes 40% Class participation 20%

off-campus students?

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Projects - Start NOW! How to start? Projects should be sufficiently challenging

but reasonable, suitable for one semester How to choose your individual project

Real-world problems Problems that might make differences

Two types of projects Available projects Self-proposed projects (Approval’s needed)

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Some project ideas Dealing with high dimensional data

Data of supervised, unsupervised learning Image mining

Feature extraction, clustering of images Active sampling

Various data structures (kd-trees, R-trees, Multi-Dimen Scaling) Meta data (RDF, namespace) for mining Ensemble learning Sequence mining (HMM learning) Bioinformatics and applications (feature selection) Intelligent driving data analysis

Data integration, data reduction (random projection)

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How is a project evaluated? It depends on

What do you want to achieve Its impact Your effort

The sooner you start, the better The beginning is not easy

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Course Web Site http://www.public.asu.edu/~huanliu/

cse591.html My office and office hours

GWC 342 T 10:30 - 11:30am and Th 4:00-5:00pm

My email: hliu@asu.edu Slides and relevant information will be

made available at the course web site

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Any questions and suggestions? Your feedback is most welcome!

I need it to adapt the course to your needs. Please feel free to provide yours anytime. Share your questions and concerns with the

class – very likely others may have the same. No pain no gain – no magic for data mining.

The more you put in, the more you get Your grades are proportional to your efforts.

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Introduction to Data Mining

DefinitionsMotivations of DM

Interdisciplinary Links of DM

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What is DM? Or more precisely KDD (knowledge

discovery from databases)? Many definitions A process, not plug-and-play

raw data transformed data preprocessed data data mining post-processing knowledge

One definition is A non-trivial process of identifying valid,

novel, useful and ultimately understandable patterns in data

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Need for Data Mining Data accumulate and double every 9 months There is a big gap from stored data to

knowledge; and the transition won’t occur automatically.

Manual data analysis is not new but a bottleneck

Fast developing Computer Science and Engineering generates new demands

Seeking knowledge from massive data Any personal experience?

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When is DM useful Data rich

Two invited talks so far have convincingly demonstrate it

Large data (dimensionality and size) Image data (size) Gene data (dimensionality)

Little knowledge about data (exploratory data analysis) What if we have some knowledge?

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DM perspectives Prediction, description, explanation, optimization,

and exploration Completion of knowledge (patterns vs. models) Understandability and representation of

knowledge Some applications

Business intelligence (CRM) Security (Info, Comp Systems, Networks, Data, Privacy) Scientific discovery (bioinformatics)

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Challenges Increasing data dimensionality and data

size Various data forms New data types

Streaming data, multimedia data Efficient search and data access Intelligent update and integration

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Interdisciplinary Links of DM

Statistics Databases AI Machine Learning Visualization High Performance Computing

supercomputers, distributed/parallel/cluster computing

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Statistics Discovery of structures or patterns in data sets

hypothesis testing, parameter estimation Optimal strategies for collecting data

efficient search of large databases Static data

constantly evolving data Models play a central role

algorithms are of a major concern patterns are sought

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Relational Databases A relational databases can contain several tables

Tables and schemas The goal in data organization is to maintain data

and quickly locate the requested data Queries and index structures

Query execution and optimization Query optimization is to find the best possible

evaluation method for a given query Providing fast, reliable access to data for data

mining

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AI Intelligent agents

Perception-Action-Goal-Environment Search

uniform cost and informed search algorithms Knowledge representation

FOL, production rules, frames with semantic networks

Knowledge acquisition Knowledge maintenance and application

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Machine Learning Focusing on complex representations, data-intensive

problems, and search-based methods Flexibility with prior knowledge and collected data Generalization from data and empirical validation

statistical soundness and computational efficiency constrained by finite computing & data recourses

Challenges from KDD scaling up, cost info, auto data preprocessing

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Visualization Producing a visual display with insights into the

structure of the data with interactive means zoom in/out, rotating, displaying detailed info

Various branches of visualization methods show summary properties and explore relationships

between variables investigate large databases and convey lots of

information analyze data with geographic/spatial location

A pre- and post-processing tool for KDD

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Bibliography W. Klosgen & J.M. Zytkow, edited, 2001,

Handbook of Data Mining and Knowledge Discovery.