DMML1_overview.ppt

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  • 1. Data Mining (and machine learning) DM Lecture 1: Overview of DM, and overview of the DM part of the DM&ML module Many of these slides are highly derivative of Nick Taylor’s slides used for this module in previous years
  • 2. Overview of My Lectures
    • All at: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html
    • 25/9 Overview of DM (and of these 8 lectures)
    • 02/10:     Data Cleaning - usually a necessary first step for large amounts of data
    • 09/10  Basic Statistics for Data Miners - essential knowledge, and very useful
    • 16/10 Basket Data/Association Rules (A Priori algorithm) - a classic algorithm, used
    • much in industry
    • NO THURSDAY LECTURE OCTOBER 23rd
    • 30/10 Cluster Analysis and Clustering - simple algs that tell you much about the data
    • NO THURSDAY LECTURE November 6th
    • 13/11: Similarity and Correlation Measures - making sure you do clustering appropriately
    • for the given data
    • 20/11: Regression - the simplest algorithm for predicting data/class values
    • 27/11: A Tour of Other Methods and their Essential Details - every important method
    • you may learn about in future
  • 3. Data Mining - Definition & Goal
    • Definition
    • – Data Mining is the exploration and analysis of 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
  • 4. Some examples of huge databases
    • Retail basket data: much commercial DM is done with this. In one store, 18,000 baskets per month
    • Tesco has >500 stores. Per year, 100,000,000 baskets ?
    • The Internet ~ >15,000,000,000 pages
    • Lots of datasets: UCI Machine Learning repository
    • How can we begin to understand and exploit such datasets? Especially the big ones?
  • 5. Like this …
  • 6. and this …
  • 7. and this …
  • 8. or this … (see http://www.cs.umd.edu/hcil/treemap-history/
  • 9. or this …
    • see
    http://websom.hut.fi/websom/milliondemo/html/root.html
  • 10. Data Mining - Basics
    • Data Mining is the process of discovering patterns and inferring associations in raw data
    • • Data Mining is a collection of powerful techniques intended to analyse large amounts of data
    • There is no single Data Mining approach
    • Data Mining can employ a range of techniques, either individually or in combination with each other
  • 11. 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
  • 12. Data Mining – History
    • The approach has its roots over 40 years ago
    • In the early 1960s Data Mining was called statistical analysis, and the pioneers were statistical software companies such as SPSS
    • By the late 1980s these traditional techniques had been augmented by new methods such as machine induction, artificial neural networks, evolutionary computing, etc.
  • 13.  
  • 14. Data Mining – Two Major Types
    • Directed (Farming) – Attempts to explain or categorise some particular target field such as income, medical disorder, genetic characteristic, etc.
    • Undirected (Exploring) – Attempts to find patterns or similarities among groups of records without the use of a particular target field or collection of predefined classes
    • Compare with Supervised and Unsupervised systems in machine learning
  • 15. Data Mining – Tasks
    • Classification - Example: high risk for cancer or not
    • Estimation - Example: household income
    • Prediction - Example: credit card balance transfer average amount
    • Affinity Grouping - Example: people who buy X, often also buy Y with a probability of Z
    • Clustering - similar to classification but no predefined classes
    • Description and Profiling – Identifying characteristics which explain behaviour - Example: “More men watch football on TV than women”
  • 16. 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
  • 17.  
  • 18. 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”
  • 19. 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
  • 20.  
  • 21. 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
  • 22. 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 unit’s acitivities
  • 23. From Data Warhousing to Machine Learning, via Data Marts
  • 24. 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
  • 25. 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
  • 26. Which brings us to “data cleaning”, next week …