Data Mining Principles (required for cw,

useful for any project…)- a reminder (?)

Based on Intro to Data Mining:

CRISP-DM

Prof Chris Clifton, Purdue UnivThanks also to Laura Squier, SPSS for some of the material

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Data Mining Process

• Cross-Industry Standard Process for Data Mining (CRISP-DM) – a Methodology, not for Software Engineering, but data-analysis work

• European Community funded effort to develop framework for data mining and text mining tasks

• Goals:– Encourage interoperable tools across entire data

mining process, by defining subtasks– Take the mystery/high-priced expertise out of simple

data mining tasks – anyone can do it! (even students)

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Why Should There be a Standard Process?

• Framework for recording experience– Allows projects to be

replicated, “real science”

• Aid to project planning and management

• “Comfort factor” for new adopters– Demonstrates maturity of

Data Mining

– Reduces dependency on “stars”

The data mining process must The data mining process must be reliable and repeatable by be reliable and repeatable by people with little data mining people with little data mining background.background.

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Why standardize the process?

• CRoss Industry Standard Process for Data Mining• Initiative launched Sept.1996• http://www.crisp-dm.org/ • SPSS/ISL, NCR, Daimler-Benz, OHRA• Funding from European commission• Over 200 members of the CRISP-DM SIG worldwide

– DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, ..

– System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, …

– End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...– Linkedin.com groups: discussion, job adverts, …

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CRISP-DM

• Non-proprietary• Application/Industry

neutral• Tool neutral• Focus on business issues

and practical problems– As well as technical

analysis• Framework for guidance• Experience base

– Templates and case studies for guidance and analysis

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CRISP-DM: Overview

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CRISP-DM: Phases• Business Understanding

– Understanding project objectives and requirements– Data mining problem definition

• Data Understanding– Initial data collection and familiarization– Identify data quality issues– Initial, obvious results

• Data Preparation– Record and attribute selection– Data cleansing

• Modeling– Run the data analysis and data mining tools

• Evaluation– Determine if results meet business objectives– Identify business issues that should have been addressed earlier

• Deployment– Put the resulting models into practice– Set up for repeated/continuous mining of the data

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BusinessUnderstanding

DataUnderstanding

EvaluationDataPreparation

Modeling

Determine Business ObjectivesBackgroundBusiness ObjectivesBusiness Success Criteria

Situation AssessmentInventory of ResourcesRequirements, Assumptions, and ConstraintsRisks and ContingenciesTerminologyCosts and Benefits

Determine Data Mining GoalData Mining GoalsData Mining Success Criteria

Produce Project PlanProject PlanInitial Asessment of Tools and Techniques

Collect Initial DataInitial Data Collection Report

Describe DataData Description Report

Explore DataData Exploration Report

Verify Data Quality Data Quality Report

Data SetData Set Description

Select Data Rationale for Inclusion / Exclusion

Clean Data Data Cleaning Report

Construct DataDerived AttributesGenerated Records

Integrate DataMerged Data

Format DataReformatted Data

Select Modeling TechniqueModeling TechniqueModeling Assumptions

Generate Test DesignTest Design

Build ModelParameter SettingsModelsModel Description

Assess ModelModel AssessmentRevised Parameter Settings

Evaluate ResultsAssessment of Data Mining Results w.r.t. Business Success CriteriaApproved Models

Review ProcessReview of Process

Determine Next StepsList of Possible ActionsDecision

Plan DeploymentDeployment Plan

Plan Monitoring and MaintenanceMonitoring and Maintenance Plan

Produce Final ReportFinal ReportFinal Presentation

Review ProjectExperience Documentation

Deployment

Phases and Tasks/Reports

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Phases in the DM Process(1)

• Business Understanding:– Statement of Business

Objective– Statement of Data

Mining objective– Statement of Success

Criteria

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Phases in cw DM Process(1)

• Business Understanding:– Business Objective: attract

Language academics to DM (to be our “customers”?)

– Data Mining objective: is domain English classed as UK or US English? (classify by salient features)

– Success Criteria: specific evidence: set of features which classify UK and US training data correctly, used to classify domain data-sets

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Phases in the DM Process(2)

• Data Understanding– Collect data– Describe data– Explore the data – Verify the quality and

identify outliers

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Phases in cw DM Process(2)

• Data Understanding– Select domain corpora to fit

region covered by journal– Describe texts: size,

sources, markup, …– Explore the texts – can you

see any obvious indications they are UK/US?

– Verify the quality (are texts really from your domain? Errors? Repetitions?) and identify outliers (texts which don’t “belong”)

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Phases in the DM Process (3)

Data preparation:

• Can take over 90% of the time

– Consolidation and Cleaning

• table links, aggregation level, missing values, etc

– Data selection

• Remove “noisy” data, repetitions, etc

• Remove outliers?

• Select samples

• visualization tools

– Transformations - create new variables, formats

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Phases in cw DM Process (3)

Data preparation:• May take up to 90% of the time• Select Data • Rationale for Inclusion /

Exclusion: if it isn‘t really from your domain – remove

• Clean Data • Remove repetitions• Remove headers, footers,

tables, pictures etc (BootCat does this automatically)

• Transform Data• Convert to plain text (ditto)• Reduce to word-frequency list,

keyword-freqs can be features in machine-learning

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Phases in the DM Process(4)

• Model building– Selection of the

modeling techniques is based upon the data mining objective

– Modeling can be an iterative process; may model for either description or prediction

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Phases in cw DM Process(4)

• Model building– Data Mining objective: is

domain English classed as UK or US English? (classify by salient features)

– “model” can be Decision Tree (or NN, or other classifier) based on freqs of UK-only terms and US-only terms (and sources used to derive these)

– Data Visualization or On-Line Analytical Processing (OLAP) as well as Data Mining

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Phases in the DM Process(5)

• Model Evaluation– Evaluation of model: how

well it performed, how well it met business needs

– Methods and criteria depend on model type:

• e.g., confusion matrix with classification models, mean error rate with regression models

– Interpretation of model: important or not, easy or hard depends on algorithm

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Phases in cw DM Process(5)

• Model Evaluation– Evaluation of model:

have you found and quantified key differences between UK, US English, to classify domain data?

– Interpretation: don’t just present the results, try to explain possible reasons

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Phases in the DM Process (6)

• Deployment– Determine how the results

need to be utilized– Who needs to use them?– How often do they need to

be used

• Deploy Data Mining results by:– Utilizing results as

business rules– Publishing report for users,

with recommendations to improve their business

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Phases in cw DM Process (6)

• Deployment– Produce a scientific

report: Intro, Methods, Results, Conclusion; PowerPoint Movie Maker YouTube

– Utilizing results as business rules: attract Language researchers to use text mining (as “customers” or collaborators for SoC researchers)

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Why CRISP-DM?

• The data mining process must be reliable and repeatable by people with little data mining skills (e.g. IT Consultants, students?...)

• CRISP-DM provides a uniform framework for – guidelines – experience documentation

• CRISP-DM is flexible to account for differences – Different business/agency problems– Different data

Why DM?: Concept Description

• Descriptive vs. predictive data mining– Descriptive mining: describes concepts or task-

relevant data sets in concise, summarative, informative, discriminative forms

– Predictive mining: Based on data and analysis, constructs models from the data-set, and predicts the trend and properties of unknown data

• Concept description: – Characterization: provides a concise and succinct

summarization of the given collection of data– Comparison: provides descriptions comparing two or

more collections of data

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DM vs. OLAP

• Data Mining: – can handle complex data types of the

attributes and their aggregations– a more automated process

• Online Analytic Processing (visualization): – restricted to a small number of dimension and

measure types– user-controlled process

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CRISP-DM: Summary• Business Understanding

– Understanding project objectives and requirements– Data mining problem definition

• Data Understanding– Initial data collection and familiarization– Identify data quality issues– Initial, obvious results

• Data Preparation– Record and attribute selection– Data cleansing

• Modeling– Run the data mining tools

• Evaluation– Determine if results meet business objectives– Identify business issues that should have been addressed earlier

• Deployment– Put the resulting models into practice– Set up for repeated/continuous mining of the data