David Corne, and Nick Taylor, Heriot-Watt University - [e-mail]

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Data Mining and Machine Learning

• Lecture 1 Why data is useful, and overview of
  DMML

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Overview of My Lectures

• http//www.macs.hw.ac.uk/dwcorne/Teaching/dmml.
  html

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Module assessment

• 100 by coursework
• Three main items of coursework,
• CW 1 30 CW 2 40 CW 3 30
• Two small items of coursework (A and B), worth
  0, but if you dont do them adequately you fail
  the module.

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Coursework submission

• ALL coursework must be submitted as follows
• as PDF
• by email to dwcorne_at_gmail.com
• the c/w is an attachment
• Subject line DMML Coursework A
• ( or B, 1, 2, 3)
• Body of the email includes your Name and your
  Course (e.g. Joe Smith, BSc CS Jill Brown, MSc
  AI)

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Office Hour Doodle Poll
http//doodle.com/ndb69faydc6ivttw

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At last, the lecture
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What some people think can be done with data

• Answer simple questions like
• How many female clients do we have?
• How much paint did we sell in 2007?
• Which is the most profitable branch of our
  supermarket?
• Which postcodes suffered the most dropped calls
  in July?

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that is so

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that is so

• Boring

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More interesting things that can be done with data

• Answer difficult and valuable questions like
• How can we predict Ovarian cancer early enough to
  treat it successfully?
• How can I make significant profit on the stock
  market next month?
• Two different authors claim to have written this
  story how can we resolve the dispute?
• How can we get our customers to spend more money
  in the store?
• Is this loan applicant a good credit risk?
• Is this sonar image a mine, or a rock?
• What other websites will this browser be
  interested in?

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Some competitions at
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Data Mining - Definition Goal

• Definition
• Data Mining is the exploration and analysis of
  (often) large quantities of data in order to
  discover meaningful patterns and rules
• Goal
• To permit some other goal to be achieved or
  performance to be improved through a better
  understanding of the data

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Some examples of large databases

• Retail basket data much commercial DM is done
  with this. In one store, 18,000 baskets per month
• Tesco has gt500 stores. Per year, 100,000,000
  baskets ?
• The Internet gt20,000,000,000 pages
• Lots of datasets UCI Machine Learning
  repository
• How can we begin to understand and exploit such
  datasets? Especially the big ones?

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Like this
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and this
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or this

• see

http//websom.hut.fi/websom/milliondemo/html/root.
html
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Or this
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Data Mining Machine Learning - Basics

• Data Mining is the process of discovering
  patterns and inferring associations in raw data
• a collection of techniques intended to analyse
  small or large amounts of data
• can employ a range of techniques, either
  individually or in combination with each other
• Machine Learning is the same, but the term ML
  emphasises a range of more sophisticated
  algorithms that try to learn accurate predictive
  models of data

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Data Mining Why is it important?

• Data are being generated in enormous quantities
• Data are being collected over long periods of
  time
• Data are being kept for long periods of time
• Computing power is formidable and cheap
• A variety of Data Mining software is available
• All of these data contain hidden knowledge
  facts, rules, patterns, that can be usefully
  exploited if we can find them.

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Some basic terminology
Gender weight height Age in mths 100m time
Male 52kg 1.71m 243 13.7s
Male 89kg 1.92m 388 22.3s
Female 48kg 1.67m 219 14.6s
Male 86kg 1.96m 274 9.58s
Male 80kg 1.88m 260 10.56s
etc etc etc etc etc
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This is called a data instance or a record or
just a line of data
Gender weight height Age in mths 100m time
Male 52kg 1.71m 243 13.7s
Male 89kg 1.92m 388 22.3s
Female 48kg 1.67m 219 14.6s
Male 86kg 1.96m 274 9.58s
Male 80kg 1.88m 260 10.56s
etc etc etc etc etc
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This is called a field or an attribute the value
of the Age field in the 4th record is 274
Gender weight height Age in mths 100m time
Male 52kg 1.71m 243 13.7s
Male 89kg 1.92m 388 22.3s
Female 48kg 1.67m 219 14.6s
Male 86kg 1.96m 274 9.58s
Male 80kg 1.88m 260 10.56s
etc etc etc etc etc
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Usually we are interested in predicting the value
of a particular field, given the values of the
other fields. What we want to predict is called
the class field, or the target class
Gender weight height Age in mths 100m time
Male 52kg 1.71m 243 13.7s
Male 89kg 1.92m 388 22.3s
Female 48kg 1.67m 219 14.6s
Male 86kg 1.96m 274 9.58s
Male 80kg 1.88m 260 10.56s
etc etc etc etc etc
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Some data-mining related projects that I am
currently working on (either myself, or with a
PhD student or RA)
Analysing flow cytometry data to detect the
presence of specific contaminants in sea-water
samples Predicting which of two or more writers
is the author of a given piece of text
Discovering which subsets of many thousands of
genes play a role in specific diseases (cancer,
diabetes, etc) Discovering technical trading
rules for stock market trading
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Who wrote text chunk 4?
0.4 0.2 0.001 0.002 0.6
AuthorA 0.3 0.15 0 0.1 0.5
AuthorA 0.2 0.2 0.001 0.002 0.5
AuthorB 0.2 0.15 0 0.002 0.6
?
Word usage Fingerprint of a 1,000 word chunk
of text
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Did the Dow Jones go up or down in the following
week?
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Down
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Will the Dow Jones go up or down tomorrow?
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Data Mining Tasks

• Classification - Example high risk for cancer or
  not
• Estimation/Prediction - Example household income
  / sales
• Association Rules- Example people who buy X,
  often also buy Y with a probability of Z
• Clustering - similar to classification but no
  predefined classes identifies meaningful
  segments of a dataset, discovers structure in
  data

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Data Warehousing

• Note that Data Mining is very generic and can be
  used for detecting patterns in almost any data
• Retail data
• Genomes
• Climate data
• Etc.
• Data Warehousing, on the other hand, is almost
  exclusively used to describe the storage of data
  in the commercial sector

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What you should do this week

• Browse the UCI Machine Learning repository
  datasets and associated information get
  acquainted with data
• Browse the statlib datasets archive, get
  acquainted with that too.
• Browse the http//www.kaggle.com/ website - to
  give you some idea of how hot data mining is
• And then

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Coursework A (0 marks, but you fail if you dont
submit an adequate attempt)

• Find three other dataset repositories as follows
• One that specialises in sports data
• One that specialises in time series data
• One that specialises in anything else that is
  interesting.
• For each of these three, tell me the URL, and
  write one paragraph, 100 words, in your own
  words, describing the contents of this
  repository,
• Submit on or before 2359pm Friday October 11th

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Au revoir
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If interested

• Some slides about data warehousing I dont
  consider this an essential part of this module,
  but in case you want to know what data
  warehousing is

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Data Warehousing - Definitions

• A subject-oriented, integrated, time-variant and
  nonvolatile collection of data in support of
  management's decision making process
• W. H. Inmon, "What is a Data Warehouse?" Prism
  Tech Topic, Vol. 1, No. 1, 1995 -- a very
  influential definition.
• A copy of transaction data, specifically
  structured for query and analysis
• Ralph Kimball, from his 2000 book, The Data
  Warehouse Toolkit

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Data Warehouse why?

• For organisational learning to take place data
  from many sources must be gathered together over
  time and organised in a consistent and useful way
• Data Warehousing allows an organisation to
  remember its data and what it has learned about
  its data
• Data Mining techniques make use of the data in a
  Data Warehouse and subsequently add their results
  to it

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Data Warehouse - Contents

• A Data Warehouse is a copy of transaction data
  specifically structured for querying, analysis
  and reporting
• The data will normally have been transformed
  when it was copied into the Data Warehouse
• The contents of a Data Warehouse, once acquired,
  are fixed and cannot be updated or changed later
  by the transaction system - but they can be added
  to of course

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Data Marts

• A Data Mart is a smaller, more focused Data
  Warehouse a mini-warehouse
• A Data Mart will normally reflect the business
  rules of a specific business unit within an
  enterprise identifying data relevant to that
  units acitivities

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From Data Warhousing to Machine Learning, via
Data Marts
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The Big Challenge for Data Mining

• The largest challenge that a Data Miner may face
  is the sheer volume of data in the Data Warehouse
• It is very important, then, that summary data
  also be available to get the analysis started
• The sheer volume of data may mask the important
  relationships in which the Data Miner is
  interested
• Being able to overcome the volume and interpret
  the data is essential to successful Data Mining

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What happens in practice

• Data Miners, both farmers and explorers, are
  expected to utilise Data Warehouses to give
  guidance and answer a limitless variety of
  questions
• The value of a Data Warehouse and Data Mining
  lies in a new and changed appreciation of the
  meaning of the data
• There are limitations though - A Data Warehouse
  cannot correct problems with its data, although
  it may help to more clearly identify them