Machine Learning Lecture BigData Analytics Julian M. Kunkel julian.kunkel@googlemail.com University of Hamburg / German Climate Computing Center (DKRZ) 2017-11-24 Disclaimer: Big Data software is constantly updated, code samples may be outdated. Introduction Methodology Classification Regression Clustering Association Rule Mining Meta-Learning Summary Outline 1 Introduction 2 Methodology 3 Classification 4 Regression 5 Clustering 6 Association Rule Mining 7 Meta-Learning 8 Summary Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 2 / 49 Introduction Methodology Classification Regression Clustering Association Rule Mining Meta-Learning Summary Data Mining (Knowledge Discovery) [1,35] Definition Data mining: process of discovering patterns in large data sets (Semi-)Automatic analysis of large data to identify interesting patterns Using artificial intelligence, machine learning, statistics and databases Tasks / Problems for data mining Classification: predict the category of samples Regression: find a function to model numeric data with the least error Anomaly detection: identify unusual data (relevant or error) Association rule learning: identify relationships between variables Clustering: discover and classify similar data into structures and groups Summarization: find a compact representation of the data Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 3 / 49 Introduction Methodology Classification Regression Clustering Association Rule Mining Meta-Learning Summary Terminology for Input Data [1, 40] Sample: instances (subset) of the unit of observation Feature: measurable property of a phenomenon (explanatory variable) The set of features is usually written as vector (f1, ..., fn) Label/response: outcome/property of interest for analysis/prediction Dependent variable Discrete in classification, continuous in regression Forms of features/labels Numeric: a (potentially discrete) number characterizes

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