Introduction to data mining

Introduction on Data Mining

What is Data Mining
Non-trivial extraction of implicit, previously unknown and potentially useful information from data
Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns
Data mining is the process of automatically discovering useful information in large data repositories
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Simple Examples for Data Mining
Predicting whether a newly arrived customer will spend more than 100$ at a department store.
Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,)

Why Data Mining
Credit ratings/targeted marketing:
Given a database of 100,000 names, which persons are the least likely to default on their credit cards?
Identify likely responders to sales promotions
Fraud detection
Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular customer?

Origins of Data Mining
Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems
Traditional Techniquesmay be unsuitable due to
Enormity of data
High dimensionality of data
Heterogeneous, distributed nature of data

Data Mining Tasks
Prediction Methods
Use some variables to predict unknown or future values of other variables
Description Methods
Find human-interpretable patterns that describe the data.

Data Mining Tasks
Classification [Predictive]
Clustering [Descriptive]
Association Rule Discovery [Descriptive]
Sequential Pattern Discovery [Descriptive]
Regression [Predictive]
Deviation Detection [Predictive]

Classification: Definition
It is used for discrete target variables
Ex: predicting whether a Web user will make a purchase at an online store is an classification tasks because the target variabe is binary-valued.

Clustering: Definition
- Clustering analysis seeks to find groups of closely related observations that belong to the same cluster are more similar to each other than observations that observations that belong s to other clusters.
Ex:
-to find areas of ocean that have aq significant impact on the earth’s climate.

Association Rule Discovery: Definition
Given a set of records each of which contain some number of items from a given collection;
Produce dependency rules which will predict occurrence of an item based on occurrences of other items.

Contd…
Rules Discovered:
{Milk} --> {Coke}
{Diaper, Milk} --> {Beer}

Sequential Pattern Discovery: Definition
Given is a set of objects, with each object associated with its own timeline of events, find rules that predict strong sequential dependencies among different events.
(A B) (C) ---> (D E)

Contd…
Rules are formed by first disovering patterns. Event occurrences in the patterns are governed by timing constraints.
(A B) (C) (D E)
<= xg
>ng
<= ws
<= ms

Sequential Pattern Discovery: Example
In telecommunications alarm logs,
(Inverter_ProblemExcessive_Line_Current)
(Rectifier_Alarm) --> (Fire_Alarm)

Regression
Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.
Greatly studied in statistics, neural network fields.

Regression-examples
Predicting sales amounts of new product based on advertising expenditure.
Predicting wind velocities as a function of temperature, humidity, air pressure, etc.
Time series prediction of stock market indices.

Deviation/Anomaly Detection
Detect significant deviations from normal behavior
Applications:
Credit Card Fraud Detection
Network Intrusion Detection

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Introduction to data mining

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