A Brief Introduction to Data Mining
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1 A Brief Introduction to Data Mining L. Torgo Departamento de Ciência de Computadores Faculdade de Ciências / Universidade do Porto Feb, 2017 What is Data Mining? Introduction A possible definition: Data Mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarise the data in novel ways that are both understandable and useful to the data owner in Principles of Data Mining (Hand et.al. 2001) L.Torgo (DCC-FCUP) DM Intro Feb, / 20
2 The McKinsey Global Institute... has predicted that by 2018 the United States could face a shortage of between 140,000 to 190,000 people with deep analytical skills, as well as a shortage of 1.5 million managers and analysts who know how to use the analysis of big data to make effective decisions.
3 Introduction So Data Mining/Science is... At the end of the day Data Science is just the Analysis of Data! Data Science Business Analytics Data Mining / Knowledge Discovery in Databases (KDD)? - surely yet another buzzword! L.Torgo (DCC-FCUP) DM Intro Feb, / 20 Introduction But is Data Science really standard data analysis?... Size L.Torgo (DCC-FCUP) DM Intro Feb, / 20
4 So Why all the Fuss about Data Science? All Sort of Cheap Sensors Ever Increasing Computing Power The Internet of Things (IoT) Networking Everywhere
5 Introduction The Typical Data Science Workflow - Different data sources - Different data types - Data munging - Feature engineering etc. - Provide reliable estimates of performance - Generic metrics - Domain-specific metrics etc. Business Understanding Data Pre- Processing Modeling Evaluation Deployment Exploratory Data Analysis Dependency Modeling Clustering Anomaly Detection Predictive Analytics L.Torgo (DCC-FCUP) DM Intro Feb, / 20
6 Data Sets Type of Data Sets Data Sets A data set is a collection of measurements taken from some environment. In the simplest case, we have p measurements for a set of n objects, i.e. a data matrix of dimension n p. The n rows represent the objects for which we have collected data. The p columns represent the measurements that were made for each object. The rows of the data matrix are also often named examples, instances, records or cases, while the columns are sometimes referred to as variables, features, fields or attributes. L.Torgo (DCC-FCUP) DM Intro Feb, / 20
7 Type of Data Sets An example of a data matrix Age Sex Area Income 45 m insurance f education f services Table: An example of a data table (matrix) L.Torgo (DCC-FCUP) DM Intro Feb, / 20 Type of Data Sets Types of Measurements Quantitative measurements Integer values Real numbers Categorical measurements Ordinal variables (implicit ordering among values - small, medium, large) Nominal variables (no order - red, blue, yellow) L.Torgo (DCC-FCUP) DM Intro Feb, / 20
8 Types of Data Sets Type of Data Sets Simple data tables (the most common situation) Databases (multiple data tables related with each other) Data streams, time series Text Multimedia data (images, sound, etc.) etc. L.Torgo (DCC-FCUP) DM Intro Feb, / 20 Models
9 Types of Models Type and structure of the models Global Local Mathematical formulae Logical formulae Black boxes etc. Different models frequently lead to different compromises in terms of understandability and predictive accuracy L.Torgo (DCC-FCUP) DM Intro Feb, / 20 Types of Models Examples of different models Logical formulae - decision rules IF amount = high AND salary = low AND employment = short.term THEN risk = high IF amount = average AND salary = high THEN risk = low Mathematical formulae housevalue = * nrrooms * distcenter * area L.Torgo (DCC-FCUP) DM Intro Feb, / 20
10 Data Mining Tasks Some of the Main Data Mining Tasks Exploratory Data Analysis summarisation and visualisation tools Descriptive Models probabilistic models clustering models association rules anomaly and deviation detection Predictive Models classification models regression models L.Torgo (DCC-FCUP) DM Intro Feb, / 20 Key Issues The Key Issues on a Data Mining Project Data Structure what to measure? pre-processing steps?... Model Structure what type of model(s) should we build?... Score Function how to evaluate the obtained models?... Optimisation and Search Method how to search and optimise the models in the context of the selected structure?... Data Management Strategy how to handle the data efficiently during model construction/evaluation?... L.Torgo (DCC-FCUP) DM Intro Feb, / 20
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