Data Mining (and machine learning)

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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? David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Like this … David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

and this … David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

or this … (see http://www.cs.umd.edu/hcil/treemap-history/ David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

or this … • see http://websom.hut.fi/websom/milliondemo/html/root.html David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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. David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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” David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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” David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

From Data Warhousing to Machine Learning, via Data Marts David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

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 David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Which brings us to “data cleaning”, next week … David Corne, and Nick Taylor, Heriot-Watt University - dwcorne@gmail.com These slides and related resources: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html

Data Mining (and machine learning)