This document discusses data mining techniques and algorithms. It defines data mining as the process of analyzing data to find hidden patterns using statistical methods. It then covers various statistical models used in data mining like decision trees, clustering, Naive Bayes, and neural networks. It lists common business uses of data mining such as recommendation generation, anomaly detection, churn analysis, and targeted advertising. Finally, it provides more details on common data mining approaches like classification, clustering, regression, forecasting, sequence analysis, and deviation analysis.

1. Data Mining
by Shaoli Lu

2. What is data mining?
• Data mining is the process of analyzing data to
find hidden patterns using automatic statistical
methodologies/algorithms/models
• Use data as the “brain”
• Predictive analytics is a subset of data mining
• Ad hoc queries and OLAP are not suited to the
task

3. Statistical Models
• Decision trees
• Clustering
• Naïve Bayes
• Neural network
• Logistic regression
• Time series
• Associations

4. Business Cases
• Recommendation generation
• Anomaly detection
• Churn analysis
• Risk management
• Customer segmentation
• Targeted ads
• Forecasting
• Data exploration

5. Common Approaches
• Classification
• Clustering
• Association
• Regression
• Forecasting
• Sequence Analysis
• Deviation Analysis

6. Classification
• Classification is the most common data mining task. Business problems such
as churn analysis, risk management, and targeted advertising usually involve
classification
• Supervised Machine Learning
• Classification is the act of assigning a category to each case
• Each case contains a set of attributes, one of which is the class attribute
• The task requires finding a model that describes the class attribute as a
function of input attributes
• Typical classification algorithms include decision trees, neural network, and
Naïve Bayes

7. Clustering
• Clustering is also called segmentation. It is used
to identify natural groupings of cases based on a
set of attributes
• Cases within the same group have more or less
similar attribute values
• Clustering is an unsupervised machine learning.
There is no single attribute used to guide the
training process, so all input attributes are
treated equally

8. Association
• Association is also called market basket analysis
• Common usage of association is to identify
common sets of items and rules for the purpose
of cross-selling
• The association task has two goals: to find those
items that appear together frequently, and from
that, to determine rules about the association

9. Regression
• The regression task is similar to classification, except
that instead of looking for patterns that describe a
class, the goal is to find patterns to determine a
numerical value
• The most popular techniques used for regression are
linear regression and logistic regression. SQL Server
supports regression trees (part of the Microsoft
Decision Trees algorithm) and neural networks
• Support categorical inputs as well as numerical
inputs

10. Forecasting
• As input, it takes sequences of numbers
indicating a series of values through time, and
then it imputes future values of those series
using a variety of machine-learning and statistical
techniques that deal with seasonality, trending,
and noisiness of data

11. Sequence Analysis
• Sequence analysis is used to find patterns in a
series of events called a sequence
• Both sequence and time-series data are similar
in that they contain adjacent observations that
are order-dependent. The difference is that
where a time series contains numerical data, a
sequence series contains discrete states

12. Deviation Analysis
• Deviation analysis is used to find rare cases that
behave very differently from the norm
• Widely used, fraud protection
• There is no standard technique for deviation
analysis, usually apply decision trees, clustering,
or neural network algorithms for this task

13. Demo
• Demo #1: Data Mining By Example – Building
Predictive Model Using Microsoft Decision Trees