Lluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining. Data Analysis and Knowledge Discovery
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1 Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining or Data Analysis and Knowledge Discovery a.k.a. Data Mining II
2 MINING as a methodology (from previous session )
3 CRISP: a DM methodology CRoss Industry Standard Process for Data Mining: neutral methodology from the point of view of industry, tool and application (free &nonproprietary) Pete Chapman, Randy Kerber (NCR); Julian Clinton, Thomas Khabaza, Colin Shearer (SPSS), Thomas Reinartz, Rüdiger Wirth (DaimlerChrysler) CRISP DM was conceived in 1996 DaimlerChrysler: leaders in industrial application, SPSS: leaders in product development (Clementine, 1994), NCR: owners of large (huge!) databases (Teradata) Financed by the EU. Version 1.0 released officially in 1999
4 CRISP: Hierarchic structure of the methodology
5 CRISP: The virtuous loop of methodology phases
6 CRISP: Phases: Problem understanding PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION DETERMINE PROBLEM GOAL BACKGROUND PROBLEM GOALS SUCCESS CRITERIA ASSESS SITUATION INVENTORY RESOURCES REQUERIMS. ASSUMPTIONS LIMITATIONS RISKS CONTINGEN. TERMINOLOG. COSTS & BENEFITS DETERMINE DM GOALS GOALS DM SUCCESS CRITERIA DM PRODUCE PROJECT PLAN PROJECT PLAN INITIAL SELECTION OF TOOLS
7 DM application areas ( 10 > 11)
8 end of last session wrap up
9 CRISP: Phases: Data understanding PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION OBTAIN INITIAL INITIAL REPORT DESCRIPTION EXPLORATION VERIFICATION QUALITY DESCRIPTIVE REPORT EXPLORATION REPORT QUALITY REPORT
10 METROFANG: a real story about data understanding (1) (Barcelona Espa%25C3%25B1a)/
11 METROFANG: a real story about data understanding (2) caudal entrada 350,00 300,00 250,00 200,00 150,00 100,00 50,00 0, Par motor Secador A 140,00 120,00 100,00 80,00 Missing data Stationality Outliers Time Series Weekend? FORUM??? 60,00 40,00 20,00 0,
12 Storing data ( 07)
13 CRISP: Phases: Data preparation PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION SELECTION ARGUMENTS FOR SELECTION CLEANING CLEANING REPORT RECONSTRUCT DERIVATED VARIABLES OSERVATIONS GENERATED INTEGRATE INTEGRATED FORMATTING WITH NEW FORMAT
14 Is data preparation that important?
15 Common data types analyzed ( 07) Compared to 2005 KDnuggets Poll on Types of data you analyzed/mined in last 12 months, the biggest increase was in anonymized data (perhaps and indicator of increasing importance of privacy issues).
16 Common data types analyzed ( 09) Compared to 2005 KDnuggets Poll on Types of data you analyzed/mined in last 12 months, the biggest increase was in anonymized data (perhaps and indicator of increasing importance of privacy issues). Comparing with 2008, the top 5 categories are unchanged.
17 Common data types analyzed ( > 12)
18 How large is it? ( 06 > 09)
19 How large is it? ( 09 > 13) The Big Data Challenge
20 How large is it? ( 09 > 13) Some fun facts: Google processes over 20 PB worth of data every day. Back in December 2007, YouTube generated 27 PB of traffic. The CERN Large Hadron Collider (HLC) generetes about 20 PB of usable data per year. The volume of global annual data traffic is expected exceed 60,000 PB in 2016, from 8,000 petabytes in 2011 In the next decade, astronomers expect to be processing 10 PB of data every hour from the Square Kilometre Array (SKA) telescope one exabyte every four days.
21 10 PB of data every hour from the Square Kilometre Array (SKA) telescope one exabyte every four days.
22 Data manipulation tools ( 08)
23 Data manipulation tools ( > 12)
24 Data manipulation tools ( > 13)
25 CRISP: Phases: Modelling PROBLEM UNDERSTANDING UNDERST ING PREPARATION MODELLING EVALUATION IMPLEMEN TATION SELECT MODELING TECHNIQUE SELECTED TECHNIQUE CREATE TEST DESIGN TEST DESIGN BUILD MODEL PARAMETER SELECTION MODEL MODEL DESCRIPTION VALIDATE MODEL MODEL VALIDATION
26 CRISP: A typology of DM problems PROBLEM DESCRIPTION EXAMPLES TECHNIQUES SUMMARY and DESCRIPTION SEGMENTATION CONCEPTUAL DESCRIPTION CLASIFICATION PREDICTION (REGRESSION, FORECASTING) DEPENDENCY ANALYSIS Compact and aggregated data description. Exploratory Analysis Finding data groups (unsupervised) segm / clust / classif Accessible and useful description of concepts / classes / groups. Knowledge comes first, then precissión. Linked to clasif / segmentation Assumed that different ítems can be assigned to a given closed cathegory (supervised) Continuous dependent variable. Given values of the predictive variables, predict (supervised) Looking for dependencies between variables (superv. or unsuperv.) Often with segmentation Almost any problem includes some elements of data description Market Segmentation, Shopping Basket analysis Ex.: Description of customer groups according to loyalty. Rule segment profiling if SEX=male and age>45 then CUST=loyal Bankruptcy prediction, Credit Scoring Markets, company benefit pred., Market share forec. Basket Analysis Ex.: 30% of those who bought peanuts also bought beer ERPs, stats., OLAP, EIS, control dashboards Clustering, NNs (SOM, GTM), visualización Rule Induction, Conceptual Clustering Discriminant Analysis, Rule Induction, Decision Trees, NNs, C-B Reasoning, GAs Regression Analysis, Regression Trees, NNs, Box-Jenkins, GAs Correlation Analysis, Association Rules, Bayesian Networks, Inductive Logic Prog.
27 CRISP: Selection of techniques U N I V E R S E OF T E C H N I Q U E S (Definided by tools) TECHNIQUES SUITED TO A PROBLEM POLITICAL REQUIREMENTS (Business, executive) Money, time, hh.rr. LIMITATIONS Data types, knowledge SELECTED TOOL(S)
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