DATA MINING (Presentation)
1
GROUP PRESENTATION BY:
RIDA WAHID
SHAFIA ASKARI
DANIYAL ARSHAD
2
OUTLINE
DBMS
DATA MINING
APPLICATIONS
RELATIONSHIP
3
DATA BASE MANAGEMENT SYSTEM
A complete system used for managing digital databases that allow storage of data, maintenance of data and searching data.
4
DATA MINING
Also known as Knowledge discovery in databases (KDD).
Data mining consists of techniques to find out hidden pattern or unknown information within a large amount of raw data.
5
EXAMPLE
An example to make it more clear:
A grocery chain used the data mining capacity of oracle software to analyze local buying patterns. They found that when men bought any item on Thursday and
Saturday, they also tended to buy beer. They further acknowledged that most of these customers did their weekly grocery shopping on Saturdays instead of
Thursdays. Hence the grocery chain could use this newly discovered info in various ways to increase revenue.
6
IMPORTANCE
Data is being produced at a phenomenal rate and so the the amount of data stored has grown.
People expect more prominent information rather then huge number of raw data lying in databases.
Data mining provides relation between large data, which is a more useful information.
7
DATA MINING vs DATA-BASE TECHNIQUE
Data Mining activities differs from Database interrogation.
Data mining identifies hidden pattern within data while
Database inquiries ask for retrieval of data.
Data mining is practiced on static data collection, called
‘DATA WAREHOUSE’, rather than ‘online’ databases which keep on updating.
8
FORMS OF DATA MINING
CLASS
DESCRIPTION
ASSOCIATION
ANAYLSIS
CLASS
DISCRIMINATION
OUTLIER
ANALYSIS
CLUSTER
ANALYSIS
CLUSTER
ANALYSIS
9
1) CLASS DESCRIPTION:
Class description deals with identifying properties that characterize a given group of data items, whereas class discrimination deals with identifying
GROUP PRESENTATION BY:
RIDA WAHID
SHAFIA ASKARI
DANIYAL ARSHAD
2
OUTLINE
DBMS
DATA MINING
APPLICATIONS
RELATIONSHIP
3
DATA BASE MANAGEMENT SYSTEM
A complete system used for managing digital databases that allow storage of data, maintenance of data and searching data.
4
DATA MINING
Also known as Knowledge discovery in databases (KDD).
Data mining consists of techniques to find out hidden pattern or unknown information within a large amount of raw data.
5
EXAMPLE
An example to make it more clear:
A grocery chain used the data mining capacity of oracle software to analyze local buying patterns. They found that when men bought any item on Thursday and
Saturday, they also tended to buy beer. They further acknowledged that most of these customers did their weekly grocery shopping on Saturdays instead of
Thursdays. Hence the grocery chain could use this newly discovered info in various ways to increase revenue.
6
IMPORTANCE
Data is being produced at a phenomenal rate and so the the amount of data stored has grown.
People expect more prominent information rather then huge number of raw data lying in databases.
Data mining provides relation between large data, which is a more useful information.
7
DATA MINING vs DATA-BASE TECHNIQUE
Data Mining activities differs from Database interrogation.
Data mining identifies hidden pattern within data while
Database inquiries ask for retrieval of data.
Data mining is practiced on static data collection, called
‘DATA WAREHOUSE’, rather than ‘online’ databases which keep on updating.
8
FORMS OF DATA MINING
CLASS
DESCRIPTION
ASSOCIATION
ANAYLSIS
CLASS
DISCRIMINATION
OUTLIER
ANALYSIS
CLUSTER
ANALYSIS
CLUSTER
ANALYSIS
9
1) CLASS DESCRIPTION:
Class description deals with identifying properties that characterize a given group of data items, whereas class discrimination deals with identifying