Data Mining & Knowledge Management for E-Commerce

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6 Νοε 2013 (πριν από 3 χρόνια και 5 μήνες)

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Data Mining & Knowledge Management

for E
-
Commerce


Kiu Ching Chieh, Prof. Dr. Woods, Dr. Beik

Introduction


Due

to

growing

popularity

of

the

electronic

commerce

driven

by

changes

in

information

technology

and

business

culture

require

the

use

of

knowledge

as

strategic

tool

for

successful

of

sustainable

competitive

advantage
.




With

information

overload

on

the

electronic

commerce

web

site,

it

is

highly

desirable

to

use

data

mining

to

extract

patterns

and

mine

the

data

from

the

databases

and

convert

this

data

into

information

and

subsequently

knowledge

so

that

the

organizations

can

use

this

knowledge

for

decision

making
.


Research Outline


This

research

focuses

how

to

extract

the

useful

knowledge

which

is

hidden

and

untapped

from

electronic

commerce

database

using

the

data

mining

technique,

integrate

knowledge

management

approach

and

addresses

a

theoretical

framework

of

knowledge
-
based

electronic

commerce
.

Searc
h
results

KDD
process
(Agent)

User Interface
(user profile)

Relevant
knowledge


KDD process (agent) reference model

Internal and
external database

Objective


Apply

knowledge

management

approach

in

the

electronic

commerce

environment
.


Use

data

mining

technique

to

manage

the

knowledge

management

process

(to

create,

gather,

organize

and

disseminate

knowledge)

in

the

electronic

commerce
.


Integrate

electronic

commerce

decision

with

the

knowledge

gained

from

knowledge

discovery

as

knowledge
-
based

electronic

commerce
.

Methodology

A
1

+

A
2

+

A
3

=

Am
1




m

………....


DB
1


DB
2


DB
3

DB
4

…………. DB
N

A1

A2

A3

Am2

R3

IIB

IKDB

Learning based on the user
profile

Browser

List of request


R = Interpreter

R(R1,R2,R3…Rn)

user

Methodology


KDD algorithms:


Case
-
base reasoning


Neural Network


Genetic Algorithms


Alternative:


Am1
: new algorithm from the combination of the existing algorithms


Am2
: new algorithm which is performed better than existing algorithms


R3

: agent that decide the best algorithm to learn and extract the appropriated
knowledge based on the user profile


Interface

To knowledge server (Intranet search & web search)

Standardization

Process (EK


SK) [Existed Knowledge


Specified Knowledge]

KB db (Knowledge base
database)

Cache the knowledge in the virtual database for indexing purpose

KM/KD/DM

An engine that mining the KB db and return the result to user

Answer the user request

Based on user profile [personalized]

Which technology is suitable for what sort of information?

T1 (I
1
, I
2
, …, A
1
, A
2
, …., K
1
, K
2
, …..)

T2 (I
3
, I
4
, …, A
3
, A
4
, …., K
3
, K
4
, …..)


New Technology (GOAL)

T3 (I
5
, I
6
, …, A
5
, A
6
, …., K
5
, K
6
, …..)


References


Amrit

Tiwana,

The

Essential

Guide

to

Knowledge

Management

:

E
-
Business

and

CRM

Applications
,

Prentice

Hall

PTR

(
2001
)


Bamshad

Mobasher,

Namit

Jain,

Eui
-
Hong

(Sam),

Jaideep

Srivastava,

Web

Mining
:

Pattern

Discovery

from

World

Wide

Web

Transactions
,

Technical

Report

96
-
050

(
1996
)



Bhavani

Thuraisingham,

Web

Data

Management

and

Electronic

Commerce
,

CRC

Press

LLC

(
2000
)


Byunghak

Leem,

En
-
Chi

Liu,

K
.
J
.

Rogers,

Knowledge
-
based

Supply

Web

Modeling

(KSWM)


Chris

Clifton,

Bhavani

Thuraisingham,

Emerging

standards

for

data

mining
,

Computer

Standards

&

Interfaces

23

(
2001
)

187
-
193


A
.
Feelders,

H
.
Daniels,

M
.

Holsheimer,

Methodological

and

practical

aspects

of

data

mining
,

Information

&

Management

37

(
2000
)

271
-
281


Gerti

Kappel,

Werner

Retschitzegger,

Birgit

Schroder,

Enabling

Technologies

for

Electronic

Commerce
.



Karl
.
M
.
Wiig,

Knowledge

Management
:

Where

Did

It

Come

From

and

Where

Will

It

Go?
,

Expert

Systems

With

Applications

13

(
1997
)

1
-
14


Michael

J
.

Shaw,

Chandrasekar

Subramaniam,

Gek

Woo

Tan,

Michael

E
.

Welge,

Knowledge

management

and

data

mining

for

marketing
,

Decision

Support

Systems

31

(
2001
)

127
-
137