What is Data Mining - Database Design - Lecture Slides, Slides of Database Management Systems (DBMS)

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Data Mining

What Is Data Mining?

Data mining is the principle of sorting through large amounts of data and picking out relevant information.

In other words…

• Data mining (knowledge discovery from data)
  • Extraction of interesting ( non-trivial, implicit, previously unknown and

potentially useful) patterns or knowledge from huge amount of data

• Other names
  • Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

Definitions Continued..

• Knowledge: Information can be converted into knowledge

about historical patterns and future trends. For example,

summary information on retail supermarket sales can be

analyzed in terms of promotional efforts to provide

knowledge of consumer buying behavior. Thus, a

manufacturer or retailer could determine which items are

most susceptible to promotional efforts.

• Data Warehouses: Data warehousing is defined as a process

of centralized data management and retrieval.

Data Warehouse example

Data Mining process

Knowledge discovery from data

KDD process includes

• data cleaning (to remove noise and inconsistent data)
• data integration (where multiple data sources may be combined)
• data selection (where data relevant to the analysis task are retrieved from the database)
• data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations)

Knowledge Discovery (KDD) Process

 Data mining—core of knowledge discovery process

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

Data Mining: Confluence of Multiple

Disciplines

Data Mining

Database

Technology

Statistics

Machine

Learning

Pattern

Recognition

Algorithm

Other

Disciplines

Visualization

Concept/Class Description: Characterization

and Discrimination

• Data Characterization: A data mining system

should be able to produce a description

summarizing the characteristics of customers.

• Example: The characteristics of customers

who spend more than $1000 a year at (some

store called ) AllElectronics. The result can be

a general profile such as age, employment

status or credit ratings.

Characterization and Discrimination

continued…

• Data Discrimination: It is a comparison of the

general features of targeting class data objects

with the general features of objects from one or a

set of contrasting classes. User can specify target and contrasting classes.

• Example: The user may like to compare the

general features of software products whose

sales increased by 10% in the last year with those

whose sales decreased by about 30% in the same

duration.

Mining Frequent Patterns, Associations and

correlations

• Another example:
• Age (X, 20…29) ^ income (X, 20K-29K) 

buys(X, “CD Player”) [Support = 2%,

confidence = 60% ]

• Customers between 20 to 29 years of age

with an income $20000-$29000. There is 60%

chance they will purchase CD Player and 2%

of all the transactions under analysis showed

that this age group customers with that range

of income bought CD Player.

Classification and Prediction

• Classification is the process of finding a model

that describes and distinguishes data classes

or concepts for the purpose of being able to

use the model to predict the class of objects

whose class label is unknown.

• Classification model can be represented in

various forms such as

» IF-THEN Rules » A decision tree » Neural network

Cluster Analysis

• Clustering analyses data objects without

consulting a known class label.

• Example: Cluster analysis can be performed on

AllElectronics customer data in order to identify

homogeneous subpopulations of customers.

These clusters may represent individual target

groups for marketing. The figure on next slide

shows a 2-D plot of customers with respect to

customer locations in a city.

Cluster Analysis