Introduction to Data Mining

1
Introduction to Data Mining

• Jiang Li
• Department of Computer Science Information
  Technology
• Austin Peay State University

2
Outline

• Data Collected
• Knowledge Discovery An Iterative Process
• Data Mining Examples
• Data Mining Functions and Algorithms

3
Data Collected

• Business
• Wal-Mart
• 20 million transactions a day
• Mobile Oil Corporation
• A 100 terabytes data warehouse
• Science
• The human genome database project
• Gigabytes of data
• NASA Earth Observing System (EOS)
• 50 gigabytes data per hour
• Radio, Television, and Film Studios
• Multimedia databases
• WWW the infinite resources
• Email huge digital libraries

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Data vs. Knowledge

• Technology is available to help us collect data
• Bar code, cameras, scanners, Radars, satellites,
  etc.
• Technology is available to help us store data
• Databases, data warehouses, variety of
  repositories
• We are swamped by data that pours on us
• We need to interpret this data in search for new
  knowledge

We are drowning in information, but starving for
knowledge. John Naisbitt
Our need is to extract interesting knowledge
(rules, regularities, patterns, constraints) from
data in large collections.
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Evolution of Database Technology

• 1960s
• Data collection, database creation (hierarchical
  and network models)
• 1970s
• Relational data model, relational DBMS
  implementation
• 1980s
• Ubiquitous RDBMS, advanced data models
  (extended-relational, Object-Oriented, deductive,
  etc.) and application-oriented DBMS (spatial,
  scientific, engineering, etc.)
• 1990s
• Data mining and data warehousing, multimedia
  databases, and Web-based database technology

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Knowledge Discovery
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Data Mining

• In theory, data mining is a step in the knowledge
  discovery process. It is the extraction of
  implicit information from a large dataset.
• In practice, data mining and knowledge discovery
  are becoming synonyms.
• KDD Knowledge Discovery and Data Mining

Notice the misnomer for data mining. Shouldnt
it be knowledge mining?
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Steps of a KDD Process

• Learning the application domain
• relevant prior knowledge and goals of application
• Gathering and integrating of data
• Cleaning and preprocessing data (may take 60 of
  effort!)
• Reducing and projecting data
• Find useful features, dimensionality/variable
  reduction,
• Choosing mining functions and algorithms
• summarization, classification, regression,
  association,
• Data mining search for patterns of interest
• Evaluating results
• Interpretation analysis of results
• visualization, alteration, removing redundant
  patterns,
• Use of discovered knowledge

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Data Mining On What Kind of Data?

• Flat Files
• Generic Data
• Relational Object-Relational Databases
• Object-Oriented Databases
• Multimedia Data
• Text Databases
• Audio, Image, and Video Databases
• Business Data
• Transactional Databases
• Engineering Data
• Spatial databases
• Temporal and Time-series databases
• WWW Data

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Data Mining Examples

• Data mining is primarily used today by companies
  with a strong consumer focus - retail, financial,
  communication, and marketing organizations.
• It enables these companies to determine
  relationships among "internal" factors such as
  price, product positioning, or staff skills, and
  "external" factors such as economic indicators,
  competition, and customer demographics.
• And, it enables them to determine the impact on
  sales, customer satisfaction, and corporate
  profits.
• Finally, it enables them to "drill down" into
  summary information to view detail transactional
  data.

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Data Mining Examples

• With data mining, a retailer could use
  point-of-sale records of customer purchases to
  send targeted promotions based on an individual's
  purchase history.
• By mining demographic data from comment or
  warranty cards, the retailer could develop
  products and promotions to appeal to specific
  customer segments.
• Blockbuster Entertainment mines its video rental
  history database to recommend rentals to
  individual customers.
• American Express can suggest products to its
  cardholders based on analysis of their monthly
  expenditures.

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Data Mining Examples

• WalMart is pioneering massive data mining to
  transform its supplier relationships.
• WalMart captures point-of-sale transactions from
  over 2,900 stores in 6 countries and continuously
  transmits this data to its massive 7.5 terabyte
  Teradata data warehouse.
• WalMart allows more than 3,500 suppliers, to
  access data on their products and perform data
  analyses.
• These suppliers use this data to identify
  customer buying patterns at the store display
  level.
• They use this information to manage local store
  inventory and identify new merchandising
  opportunities.

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Business Data Mining Examples

• The NBA is exploring a data mining application
  that can be used in conjunction with image
  recordings of basketball games.
• The Advanced Scout software analyzes the
  movements of players to help coaches orchestrate
  plays and strategies.
• For example, an analysis of the play-by-play
  sheet of the game played between the New York
  Knicks and the Cleveland Cavaliers on January 6,
  1995 reveals that when Mark Price played the
  Guard position, John Williams attempted four jump
  shots and made each one!
• A coach can automatically bring up the video
  clips showing each of the jump shots attempted by
  Williams with Price on the floor, without needing
  to comb through hours of video footage.
• Those clips show a very successful pick-and-roll
  play in which Price draws the Knick's defense and
  then finds Williams for an open jump shot.

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Data Mining Functions and Algorithms

• Association Rules
• Data can be mined to identify associations.
• The butter-gtbread example is an example of
  associative mining.
• To find rules like inside(x, city) à near(x,
  highway).
• Classification and Prediction
• Classify data based on the values in a
  classifying attribute, e.g.,
• classify countries based on climate
• classify cars based on gas mileage
• Stored data is used to locate data in
  predetermined groups.
• A restaurant chain could mine customer purchase
  data to determine when customers visit and what
  they typically order. This information could be
  used to increase traffic by having daily specials.

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Data Mining Functions and Algorithms

• Clustering
• Data items are grouped according to logical
  relationships or consumer preferences.
• Data can be mined to identify market segments or
  consumer affinities.
• To cluster houses to find distribution patterns.
• Sequential patterns
• Data is mined to anticipate behavior patterns and
  trends.
• An outdoor equipment retailer could predict the
  likelihood of a backpack being purchased based on
  a consumer's purchase of sleeping bags and hiking
  shoes.
• To find and characterize similar sequences and
  deviation data, e.g., stock analysis.
• To find segment-wise or total cycles or periodic
  behaviors in time-related data.

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Data Mining Linear Classification

• A simple linear classification boundary for the
  loan data set shaded region denotes class no
  loan

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Data Mining - Confluence of Multiple Disciplines