IRDS: Data Mining Process

Transcription

1 IRDS: Data Mining Prcess Charles Suttn University f Edinburgh (many figures used frm Murphy. Machine Learning: A Prbabilistic Perspective.)

2 Data Science Our wrking definitin Data science is the study f the cmputatinal principles, methds, and systems fr extracting knwledge frm data. A relatively new term. A lt f current hype If yu have t put science in the name Cmpnent areas have a lng histry machine learning databases statistics ptimizatin natural language prcessing cmputer visin speech prcessing applicatins t science, business, health. Difficult t find anther term fr this intersectin

3 The term data mining Data mining is the analysis f (ften large) bservatinal data sets t find unsuspected relatinships and t summarise the data in nvel ways that are bth understandable and useful t the data wner. Hand, Mannila, Smyth, 2001

4 The term data mining Data mining is the analysis f (ften large) bservatinal data sets t find unsuspected relatinships and t summarise the data in nvel ways that are bth understandable and useful t the data wner. Hand, Mannila, Smyth, 2001 nt cllected fr the purpse f yur analysis

5 The term data mining Data mining is the analysis f (ften large) bservatinal data sets t find unsuspected relatinships and t summarise the data in nvel ways that are bth understandable and useful t the data wner. Hand, Mannila, Smyth, 2001 Many easy patterns already knwn e.g., pregnant example frm assciatin rule mining

6 The term data mining Data mining is the analysis f (ften large) bservatinal data sets t find unsuspected relatinships and t summarise the data in nvel ways that are bth understandable and useful t the data wner. Hand, Mannila, Smyth, 2001 Tradeff between predictive perfrmance human interpretability Ex: neural netwrks vs decisin trees

7 Befre I get t far ahead f myself

8 What prblem am I trying t slve?

9 Prblem Types Visualizatin Predictin: Learn a map x > y Classificatin: Predict categrical value Regressin: Predict a real value Others Cllabrative filtering Learning t rank Structured predictin Descriptin Clustering Dimensinality reductin Density estimatin Finding patterns Assciatin rule mining Detecting anmalies / utliers supervised learning unsupervised learning

10 Predictin Examples Classificatin Advertising Ex: Given the text f an nline advertisement and a search engine query, predict whether a user will click n the ad Dcument classificatin Ex: Spam filtering Object detectin Ex: Given an image patch, dse it cntain a face? Regressin Predict the final vte in an electin (r referendum) frm plls Predict the temperature tmrrw given the previus few days Smetimes augmented with ther structure / infrmatin Structured predictin Spatial data, Time series data Ex: Predicting cding regins in DNA Cllabrative filtering (Amazn, Netflix) Semi-supervised learning

11 Descriptin Examples Clustering Assign data int grups with high intra-grup similarity (like classificatin, except withut examples f crrect grup assignments) Ex: Cluster users int grups, based n behaviur Scial netwrk analysis Autclass system (Cheeseman et al. 1988) discvered a new type f star, Dimensinality reductin Eigenfaces Tpic mdelling Discvering graph structure Ex: Transcriptin netwrks Ex: JamBayes fr Seattle traffic jams Assciatin rule mining Market basket data Cmputer security

12 Data Analysis Prcess Phrase as a machine learning prblem Prepare, clean data Cllect data Select features Mre data Better features Train mdel Different learning algrithm Evaluate mdel Perfrmance gd enugh? N Decide hw t imprve mdel Inspired by Wagstaff, Machine Learning that Matters Yes Deply mdel N Fr anther mre industrial prcess, see CRISP-DM. Mnitr deplyed mdel Perfrmance still gd? Yes

13 Radmap In the next few weeks, we ll talk abut Visualizatin Feature extractin Evaluatin and debugging But t talk abut these, we still need t understand representatin behind the algrithms

14 Tw Representatin Prblems Data Feature Vectrs Mdels/Patterns Image Classificatin x2 Feature Extractin x Learning X w x y x x x x x x1 x Dcument Clustering Feature Extractin Learning X Assciatin Rule Mining Transactin Database Feature Extractin Learning X if Diapers = 1 then Beer = 1 if Ham = 1 then Pineapple = What Givenisinput, the set what f pssible ges in the mdels? feature vectr?

15 Tw Representatin Prblems 1. What features t use 2. What is the space f pssible mdels In these lectures, we discuss features. Fr mdel, see > IAML, PMR, MLPR But: T pick features, must understand mdel. S: Whirlwind tur f mdels, leaving ut learning algrithms

16 Summary Different types f mdel structures 1. Linear bundaries (fr classificatin and regressin) 2. Nnlinear bundaries (but linear in a set f features) 3. Wavy bundaries (nnparametric, piecewise linear) 4. Cnvex bundaries (with respect t Euclidean distance) This will affect feature cnstructin, sn.