Introduction to Data Mining and MachineLearning TechniquesIza Moise, Evangelos Pournaras, Dirk Helbing
Iza Moise, Evangelos Pournaras, Dirk Helbing 1
Overview
Main principles of data miningDefinitionSteps of a data mining processSupervised vs. unsupervised data mining
Applications
Data mining functionalities
Iza Moise, Evangelos Pournaras, Dirk Helbing 2
Definition
Data mining
is the automated process of discovering interesting (non-trivial, pre-viously unknown, insightful and potentially useful) information orpatterns, as well as descriptive, understandable, and predictive mod-els from large-scale data.
Also known as:
• Knowledge discovery in databases (KDD)
• Data analysis
• Information harvesting
• Business intelligence
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Goals
X search consistent patterns and/or systemic relationshipsbetween data
X validate the findings by applying the detected patterns to newsubsets of data
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Data Mining is...
• k-means clustering
• decision trees
• neural networks
• Bayesian networks
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Data Mining is not...
• Data warehousing
• SQL / Ad Hoc Queries / Reporting
• Software Agents
• Online Analytical Processing (OLAP)
• Data Visualization
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Knowledge Discovery Process
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Steps of a data mining process
1. Exploration
2. Model building and validation
3. Deployment
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Exploration
• data preparation→ data cleaning, data transformation etc.
• elaborate exploratory analyses using a wide variety ofgraphical and statistical methods, depending on the nature ofthe analytic problem
• determine the general nature of models that can be taken intoaccount in the next stage
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Model building and validation, and Deployment
• Model building and validation:→ consider various models and choose the best one→ validate the model on existing data
• Deployment:→ apply the model to new data
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Model building and validation, and Deployment
• Model building and validation:→ consider various models and choose the best one→ validate the model on existing data
• Deployment:→ apply the model to new data
Iza Moise, Evangelos Pournaras, Dirk Helbing 10
Model building and validation, and Deployment
• Model building and validation:→ consider various models and choose the best one→ validate the model on existing data
• Deployment:→ apply the model to new data
Iza Moise, Evangelos Pournaras, Dirk Helbing 10
Two styles of data mining: Predictive andDescriptive
1. Predictive→ Predict the value of a specific attribute (target or dependent)based on the value of other attributes (explanatory)
2. Descriptive→ Derive patterns that summarise the relationships betweendata
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Supervised vs. Unsupervised
Supervised
• predictive or directed
• useful when you have a specifictarget value to predict aboutyour data
• Classification, Regression,Anomaly Detection
Unsupervised
• descriptive or undirected
• finds hidden structure andrelation within the data
• Clustering, Association, FeatureExtraction
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Supervised Data Mining
• a pre-specified target variable
• explanatory variables ↔ one (or more) dependent variables
• the algorithm is given many training data where the target isknown
• the algorithm identifies the “new” data points that match themodel of each target value
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Unsupervised Data Mining
• determine the existence of classes or clusters in the data
• exploratory analysis
• all variable are treated in the same way
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Overview
Main principles of data miningDefinitionSteps of a data mining processSupervised vs. unsupervised data mining
Applications
Data mining functionalities
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Fielded Applications
• Web mining→ PageRank, Search Engine, Recommenders, Social Networks
• Screening Images
• Diagnosis• Marketing and Sales
→ Market basket analysis, Direct marketing
• Biology, biomedicine, astronomy, chemistry
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Beers and Diapers
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Overview
Main principles of data miningDefinitionSteps of a data mining processSupervised vs. unsupervised data mining
Applications
Data mining functionalities
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1. Supervised Data MiningX ClassificationX RegressionX Outlier detectionX Frequent pattern mining
2. Unsupervised Data MiningX ClusteringX Feature Extraction
→ definition
→ real use-cases
→ method
→ pros and cons
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1. Supervised Data MiningX ClassificationX RegressionX Outlier detectionX Frequent pattern mining
2. Unsupervised Data MiningX ClusteringX Feature Extraction
→ definition
→ real use-cases
→ method
→ pros and cons
Iza Moise, Evangelos Pournaras, Dirk Helbing 19