Samenvatting
Summary Data Mining for Business & Governance full course
Summary of 133 pages for the course Data Mining For Business And Governance at UVT (Full course notes)
[Meer zien]Voorbeeld 4 van de 133 pagina's
Voorbeeld 4 van de 133 pagina's
In winkelwagengesponsord bericht van onze partner
Voorbeeld van de inhoud
DATA MINING FOR BUSINESS AND GOVERNANCEChris Emmery, Çiçek Güven & Gonzalo Nápoles
TABLE OF CONTENTS
Introduction to Data Mining ........................................................................................................................... 5
1. What is Data Mining? ................................................................................................................................ 5
1.1. Key aspects: Computation & Large data sets .................................................................................... 5
1.2. Big Data ............................................................................................................................................. 6
1.3. Applications ....................................................................................................................................... 6
2. What makes prediction possible?............................................................................................................... 6
3. Data Mining as Applied Machine Learning ................................................................................................ 7
3.1. Supervised learning ........................................................................................................................... 7
3.2. Unsupervised Learning ...................................................................................................................... 8
Introduction to Data Science ......................................................................................................................... 10
1. What is data science?............................................................................................................................... 10
1.1. Example ........................................................................................................................................... 10
1.2. Terminology..................................................................................................................................... 10
1.3. The algorithm .................................................................................................................................. 12
1.4. Evaluation ........................................................................................................................................ 12
1.5. Computer hardware ........................................................................................................................ 13
2. Representing data .................................................................................................................................... 14
2.1. How do we get data? ....................................................................................................................... 14
2.2. File formats: raw-level representation of files ................................................................................ 15
2.3. Databases: storing the data a bit more cleverly .............................................................................. 16
2.4. Data science in practice: 80% vs. 20% ............................................................................................. 16
2.5. Representation of data .................................................................................................................... 16
Articles week 1 ............................................................................................................................................. 17
Prediction (SL): regression & classification .................................................................................................... 20
1. What makes prediction possible?............................................................................................................. 20
1.1. Correlation Coefficient: Pearson’s r................................................................................................. 20
2. Regression ................................................................................................................................................ 23
3. Classification ............................................................................................................................................ 24
3.1. Decision boundaries to label parts of a data as being a certain category ....................................... 26
3.2. ML algorithms for classification using decision boundaries ............................................................ 26
3.3. Multiclass classification (ó binary classification) ........................................................................... 35
4. Fitting and tuning ..................................................................................................................................... 36
4.1. Fitting............................................................................................................................................... 37
1
, 4.2. Tuning .............................................................................................................................................. 38
5. Evaluation ................................................................................................................................................ 43
5.1. Metrics for evaluating a Regression Task ........................................................................................ 43
5.2. Metrics for evaluating a Classification Task..................................................................................... 43
5.3. Schemes for applying metrics in model selection ........................................................................... 46
5.4. Best practices & common pitfalls .................................................................................................... 49
6. Models ...................................................................................................................................................... 55
6.1. Model selection ............................................................................................................................... 55
6.2. What is ‘learning’? ........................................................................................................................... 55
Working with Text data ................................................................................................................................ 56
1. Representing text as vectors .................................................................................................................... 56
1.1. Converting to numbers .................................................................................................................... 56
2. Binary vectors for Decision Tree classification (ID3) ................................................................................. 58
2.1. Inferring rules (decisions) by information gain: EX: Spam detection .............................................. 58
3. Using Vector Spaces and weightings ........................................................................................................ 62
3.1. Binary vs. Frequency........................................................................................................................ 62
3.2. Term frequencies............................................................................................................................. 62
3.3. (Inverse) document frequency ........................................................................................................ 64
3.4. Putting it together: tf * idf weighting............................................................................................... 64
3.5. Normalizing vector representations ................................................................................................ 65
4. Document classification using 𝑘-NN ........................................................................................................ 66
4.1. 𝓵𝟐 normalization ............................................................................................................................. 66
4.2. Cosine similarity .............................................................................................................................. 67
4.3. Using similarity in 𝒌-nn.................................................................................................................... 67
5. Practical examples.................................................................................................................................... 70
5.1. Naive text cleaning .......................................................................................................................... 70
6. Document classification ........................................................................................................................... 73
6.1. Sentiment analysis ........................................................................................................................... 73
6.2. Build a model ................................................................................................................................... 75
6.3. Test our model ................................................................................................................................ 82
Dimensionality reduction .............................................................................................................................. 83
1. The importance of dimensions ................................................................................................................. 83
2. Visualization ............................................................................................................................................. 85
2.1. Box plots .......................................................................................................................................... 85
2.2. Histogram ........................................................................................................................................ 85
2.3. Scatter plots..................................................................................................................................... 85
3. Dimensionality reduction ......................................................................................................................... 86
3.1. Feature selection ............................................................................................................................. 86
3.2. Feature extraction ........................................................................................................................... 88
4. Deep neural networks .............................................................................................................................. 90
Unsupervised learning .................................................................................................................................. 91
2
, 1. Techniques................................................................................................................................................ 92
1.1. CRISP trough k-means algorithm (most important method) ........................................................... 92
1.2. Fuzzy trough Fuzzy c-means algorithm............................................................................................ 93
1.3. Hierarchical clustering ..................................................................................................................... 95
2. Distance function...................................................................................................................................... 96
3. Evaluation method ................................................................................................................................... 97
3.1. The Silhouette coefficient/score ..................................................................................................... 97
3.2. Dunn index ...................................................................................................................................... 97
Association mining........................................................................................................................................ 98
1. Measures: support & confidence .............................................................................................................. 99
1.1. Support ............................................................................................................................................ 99
1.2. Confidence....................................................................................................................................... 99
2. Mining association rules......................................................................................................................... 100
3. A priori algorithm ................................................................................................................................... 101
3.1. The algorithm ................................................................................................................................ 101
3.2. Considerations ............................................................................................................................... 102
3.3. Setting the support parameter (minsup)....................................................................................... 102
3.4. Pattern evaluation ......................................................................................................................... 103
4. Itemset taxonomy .................................................................................................................................. 104
4.1. Maximal frequent itemset ............................................................................................................. 104
4.2. Closed itemset ............................................................................................................................... 104
4.3. Maximal vs. closed......................................................................................................................... 105
5. Quantitative association rules ................................................................................................................ 105
Mining massive data ................................................................................................................................... 107
1. Parallelization......................................................................................................................................... 107
1.1. Requirements ................................................................................................................................ 108
1.2. How does parallelization work? .................................................................................................... 109
2. Bagging, Boosting, and Batching ........................................................................................................... 111
2.1. Boosting (ex. AdaBoost) ................................................................................................................ 111
2.2. Averaging (ex. Bagging, Random Forests) ..................................................................................... 113
2.3. Batching (online learning) ............................................................................................................. 115
2.4. Drawbacks of ensemble methods ................................................................................................. 116
3. Distributed Computing ........................................................................................................................... 117
3.1. Distributing Machine Learning models .......................................................................................... 117
3.2. Distributed file storage .................................................................................................................. 118
3.3. Map reduce ................................................................................................................................... 119
Deep learning ............................................................................................................................................. 121
1. A brief history of AI ................................................................................................................................. 121
1.1. Alan Turing .................................................................................................................................... 121
1.2. Sci-project (1974) .......................................................................................................................... 122
1.3. The Sojourner Rover (1997) .......................................................................................................... 123
1.4. “Sub-symbolic” AI (1988-2016) ..................................................................................................... 123
3
, 2. Recognizing patterns .............................................................................................................................. 123
2.1. Neural networks ............................................................................................................................ 123
2.2. McCulloch-Pitts Neurons (1947).................................................................................................... 125
2.3. Deep Learning (2015) .................................................................................................................... 126
3. Many successes of DL ............................................................................................................................. 131
4. Conclusion .............................................................................................................................................. 133
4
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper clairevanroey. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €9,89. Je zit daarna nergens aan vast.
Is Stuvia te vertrouwen?
4,6 sterren op Google & Trustpilot (+1000 reviews)
Afgelopen 30 dagen zijn er 82191 samenvattingen verkocht
Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen