Data Mining

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CS499/699-10 Data Mining

• Fall 2003
• Professor Guozhu Dong
• Computer Science Engineering
• WSU

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Introduction

• Introduction to this Course
• Introduction 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, Business,
• 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 skills
• How is this course conducted?
• Home works, projects, exams, classes
• Course Format
• Individual Projects 30
• Exams and/or quizzes 60
• Homeworks 10

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Course Web Site

• cs.wright.edu/gdong/mining03/WSUCS499DataMining.h
  tm
• My office and office hours
• RC 430
• 430-530, T Th
• My email gdong_at_cs.wright.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

• Definitions
• Motivations of DM
• Interdisciplinary Links of DM

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What is DM?

• Or more precisely KDD (knowledge discovery from
  databases)?
• Many definitions
• An iterative 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 wont 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 world
• Large data (dimensionality and size)
• Image data (size)
• Gene chip data (dimensionality)
• Little knowledge about data (exploratory data
  analysis)
• What if we have some knowledge?

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DM perspectives

• KDD goals Prediction, description,
  explanation, optimization, and exploration
• Knowledge forms patterns vs. models
• Understandability and representation of knowledge
• Some applications
• Business intelligence (CRM)
• Security (Info, Comp Systems, Networks, Data,
  Privacy)
• Scientific discovery (bioinformatics, medicine)

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Challenges

• Increasing data dimensionality and data size
• Various data forms
• New data types
• Streaming data, multimedia data
• Efficient search and access to data/knowledge
• 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 database 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 resources
• Challenges from KDD
• scaling up, cost info, auto data preprocessing,
  more knowledge types

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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 types of visualization methods
• show summary properties and explore relationships
  between variables
• investigate large DBs and convey lots of
  information
• analyze data with geographic/spatial location
• A pre- and post-processing tool for KDD

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Bibliography

• J. Han and M. Kamber. Data Mining Concepts and
  Techniques. 2001. Morgan Kaufmann.
• D. Hand, H. Mannila, P. Smyth. Principals of Data
  Mining. 2001. MIT.
• W. Klosgen J.M. Zytkow, edited, 2001, Handbook
  of Data Mining and Knowledge Discovery.