· · The importance of data mining queries

·      
Project
aims for efficient content based retrieval        process of relative temporal pattern
using signature based indexing method.

 

·      
 We
propose a system with signature-based indexing method to speed up content based
queries on temporal patterns.

 

·      
It is used
to optimize the storage and retrieval of a large collection of relative
temporal patterns.

 

·      
 The
use of signature files improves the performance of temporal pattern retrieval.

 

·      
This
retrieval system is currently being combined with visualization techniques for
monitoring the behavior of a single pattern or a group of patterns over time.

 

 

 

 

·     Scope Of The Project:

 

·      
MANY
rule discovery algorithms in data mining generate a large number of patterns/rules,
sometimes even exceeding the size of the underlying database, with only a small
fraction being of interest to the user.

 

·      
It is
generally understood that interpreting the discovered patterns/rules to gain
insight into the domain is an important phase in the knowledge discovery
process. 

 

·      
A more
flexible approach is to allow the identification of rules that are of special
importance to the user through templates or data mining queries. 

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query data and
query patterns, rules, and models extracted from these data.

 

·      
 Therefore,
to be useful, a data mining system must manage the generated rules by offering
flexible tools for rule selection. 

·      
 In
the case of association rule mining, several approaches for the post processing
of discovered association rules have been discussed.

 

·      
One
approach is to group “similar rules which works well for a moderate number of
rules.

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query the data
and query patterns, rules, and models extracted from these data.

 

·      
For
example, providing the user with a list of association rules ranked by their
confidence and support might not be a good way of organizing the set of rules
as this method would overwhelm the user and not all rules with high confidence
and support are necessarily interesting for a variety of reasons.

 

·      
Mine-Rule
DMQL and OLE DB :

These languages are designed to generate the
rules   from the data rather than allow
queries over the discovered rules.

 

·       
Focuses
on supporting content-based queries of temporal patterns, as opposed to point
or range based queries.

·       
Efficiently
retrieving subsets of a large collection of previously discovered temporal
patterns.

 

·       
Basic
Requriement:

 

Ø
    Modules and Description Of
Signature Based Indexing Module.

 

·       
Finding
temporal pattern similarity .

·       
 Constructing Signature Files for temporal
patterns .

·       
 Answering Content-Based Queries.

                                                               

·       
Finding
temporal pattern similarity :

 

·       
In this
module we are maintain the temporal pattern which are variable-length objects
that cannot be represented in a k-dimensional metric space.

 

·       
 Each pattern contains a list of states.

 

·       
Each pattern
contains a set of state relationships.

 

·     
Constructing
Signature Files for temporal patterns :

 

·       Let D be a temporal pattern database and q be
a query pattern.

·       The four forms of content based queries that
this research supports include the following:

ü Sub pattern queries. Find those patterns in D
that contain q.

ü  2.
Super pattern queries. Find those patterns in D that are a sub pattern of q.

ü 3. Equality queries. Find those patterns in D
equal to q.

ü 4. K-nearest sub pattern queries. Find the k
most similar patterns in D to q. 

 

·      
Answering
Content-Based Queries:

·      
 In
this module the signature file is bit representation of query, i.e. equivalent
set is converted into hash function. 

 

 ·      
Project
aims for efficient content based retrieval        process of relative temporal pattern
using signature based indexing method.

 

·      
 We
propose a system with signature-based indexing method to speed up content based
queries on temporal patterns.

 

·      
It is used
to optimize the storage and retrieval of a large collection of relative
temporal patterns.

 

·      
 The
use of signature files improves the performance of temporal pattern retrieval.

 

·      
This
retrieval system is currently being combined with visualization techniques for
monitoring the behavior of a single pattern or a group of patterns over time.

 

 

 

 

·     Scope Of The Project:

 

·      
MANY
rule discovery algorithms in data mining generate a large number of patterns/rules,
sometimes even exceeding the size of the underlying database, with only a small
fraction being of interest to the user.

 

·      
It is
generally understood that interpreting the discovered patterns/rules to gain
insight into the domain is an important phase in the knowledge discovery
process. 

 

·      
A more
flexible approach is to allow the identification of rules that are of special
importance to the user through templates or data mining queries. 

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query data and
query patterns, rules, and models extracted from these data.

 

·      
 Therefore,
to be useful, a data mining system must manage the generated rules by offering
flexible tools for rule selection. 

·      
 In
the case of association rule mining, several approaches for the post processing
of discovered association rules have been discussed.

 

·      
One
approach is to group “similar rules which works well for a moderate number of
rules.

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query the data
and query patterns, rules, and models extracted from these data.

 

·      
For
example, providing the user with a list of association rules ranked by their
confidence and support might not be a good way of organizing the set of rules
as this method would overwhelm the user and not all rules with high confidence
and support are necessarily interesting for a variety of reasons.

 

·      
Mine-Rule
DMQL and OLE DB :

These languages are designed to generate the
rules   from the data rather than allow
queries over the discovered rules.

 

·       
Focuses
on supporting content-based queries of temporal patterns, as opposed to point
or range based queries.

·       
Efficiently
retrieving subsets of a large collection of previously discovered temporal
patterns.

 

·       
Basic
Requriement:

 

Ø
    Modules and Description Of
Signature Based Indexing Module.

 

·       
Finding
temporal pattern similarity .

·       
 Constructing Signature Files for temporal
patterns .

·       
 Answering Content-Based Queries.

                                                               

·       
Finding
temporal pattern similarity :

 

·       
In this
module we are maintain the temporal pattern which are variable-length objects
that cannot be represented in a k-dimensional metric space.

 

·       
 Each pattern contains a list of states.

 

·       
Each pattern
contains a set of state relationships.

 

·     
Constructing
Signature Files for temporal patterns :

 

·       Let D be a temporal pattern database and q be
a query pattern.

·       The four forms of content based queries that
this research supports include the following:

ü Sub pattern queries. Find those patterns in D
that contain q.

ü  2.
Super pattern queries. Find those patterns in D that are a sub pattern of q.

ü 3. Equality queries. Find those patterns in D
equal to q.

ü 4. K-nearest sub pattern queries. Find the k
most similar patterns in D to q. 

 

·      
Answering
Content-Based Queries:

·      
 In
this module the signature file is bit representation of query, i.e. equivalent
set is converted into hash function. 

 

 ·      
Project
aims for efficient content based retrieval        process of relative temporal pattern
using signature based indexing method.

 

·      
 We
propose a system with signature-based indexing method to speed up content based
queries on temporal patterns.

 

·      
It is used
to optimize the storage and retrieval of a large collection of relative
temporal patterns.

 

·      
 The
use of signature files improves the performance of temporal pattern retrieval.

 

·      
This
retrieval system is currently being combined with visualization techniques for
monitoring the behavior of a single pattern or a group of patterns over time.

 

 

 

 

·     Scope Of The Project:

 

·      
MANY
rule discovery algorithms in data mining generate a large number of patterns/rules,
sometimes even exceeding the size of the underlying database, with only a small
fraction being of interest to the user.

 

·      
It is
generally understood that interpreting the discovered patterns/rules to gain
insight into the domain is an important phase in the knowledge discovery
process. 

 

·      
A more
flexible approach is to allow the identification of rules that are of special
importance to the user through templates or data mining queries. 

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query data and
query patterns, rules, and models extracted from these data.

 

·      
 Therefore,
to be useful, a data mining system must manage the generated rules by offering
flexible tools for rule selection. 

·      
 In
the case of association rule mining, several approaches for the post processing
of discovered association rules have been discussed.

 

·      
One
approach is to group “similar rules which works well for a moderate number of
rules.

 

·      
The
importance of data mining queries has been highlighted by the introduction of
the inductive database concept, which allows the user to both query the data
and query patterns, rules, and models extracted from these data.

 

·      
For
example, providing the user with a list of association rules ranked by their
confidence and support might not be a good way of organizing the set of rules
as this method would overwhelm the user and not all rules with high confidence
and support are necessarily interesting for a variety of reasons.

 

·      
Mine-Rule
DMQL and OLE DB :

These languages are designed to generate the
rules   from the data rather than allow
queries over the discovered rules.

 

·       
Focuses
on supporting content-based queries of temporal patterns, as opposed to point
or range based queries.

·       
Efficiently
retrieving subsets of a large collection of previously discovered temporal
patterns.

 

·       
Basic
Requriement:

 

Ø
    Modules and Description Of
Signature Based Indexing Module.

 

·       
Finding
temporal pattern similarity .

·       
 Constructing Signature Files for temporal
patterns .

·       
 Answering Content-Based Queries.

                                                               

·       
Finding
temporal pattern similarity :

 

·       
In this
module we are maintain the temporal pattern which are variable-length objects
that cannot be represented in a k-dimensional metric space.

 

·       
 Each pattern contains a list of states.

 

·       
Each pattern
contains a set of state relationships.

 

·     
Constructing
Signature Files for temporal patterns :

 

·       Let D be a temporal pattern database and q be
a query pattern.

·       The four forms of content based queries that
this research supports include the following:

ü Sub pattern queries. Find those patterns in D
that contain q.

ü  2.
Super pattern queries. Find those patterns in D that are a sub pattern of q.

ü 3. Equality queries. Find those patterns in D
equal to q.

ü 4. K-nearest sub pattern queries. Find the k
most similar patterns in D to q. 

 

·      
Answering
Content-Based Queries:

·      
 In
this module the signature file is bit representation of query, i.e. equivalent
set is converted into hash function.