datamining overview

Author: rasbt

README.md

@@ -90,6 +90,7 @@ Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/prefac
 - [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](./faq/bagging-boosting-rf.md)
 - [What are the disadvantages of using classic decision tree algorithm for large dataset?](./faq/decision-tree-disadvantages.md)
 - [Is it always better to have the largest possible number of folds when performing cross validation?](./faq/number-of-kfolds.md)
+- [What are the different fields of study in data mining?](./faq/datamining-overview.md)
 - [Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?](./faq/decision-tree-binary.md)
 - [What is the probabilistic interpretation of regularized logistic regression?](./faq/probablistic-logistic-regression.md)
 - [Can you give a visual explanation for the back propagation algorithm for neural networks?](./faq/visual-backpropagation.md)

faq/README.md

@@ -33,6 +33,7 @@ Sebastian
 - [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](./bagging-boosting-rf.md)
 - [What are the disadvantages of using classic decision tree algorithm for large dataset?](./decision-tree-disadvantages.md)
 - [Is it always better to have the largest possible number of folds when performing cross validation?](./number-of-kfolds.md)
+- [What are the different fields of study in data mining?](./datamining-overview.md)
 - [Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?](./decision-tree-binary.md)
 - [What is the probabilistic interpretation of regularized logistic regression?](./probablistic-logistic-regression.md)
 - [Can you give a visual explanation for the back propagation algorithm for neural networks?](./visual-backpropagation.md)

faq/datamining-overview.md

@@ -0,0 +1,21 @@
+# What are the different fields of study in data mining?
+
+
+I would roughly define the different application areas as
+
+1) Clustering (unsupervised learning)
+e.g., to find groups of customers based on some similarity
+
+2) Predictive modeling (supervised learning)
+2.1) Classification
+        e.g., medical diagnosis (sick/healthy), image classification etc.
+2.2) Regression
+      e.g., stock trade change prediction
+2.3) Ranking
+      e.g., search engine results
+
+3) Association rule mining
+e.g., which products do customers frequently buy together
+
+4) Anomaly detection
+e.g., credit fraud detection