Data Mining for Actionable Knowledge: A Survey - Semantic Scholar

Recommend Documents

Actionable Knowledge Discovery for Threats ... - Semantic Scholar

multi-dimensional knowledge discovery method. Keywords: Internet threat intelligence, domain-driven data mining, knowledge discovery. 1. Introduction.

A Survey of Data Mining Techniques for Steganalysis - Semantic Scholar

In this study, we will present the survey of using data mining techniques on steganalysis, ... clustering and other data mining methods that have been applied for ...

A Survey of Data Mining Techniques for Steganalysis - Semantic Scholar

Farid Ghareh Mohammadi and Mohammad Saniee Abadeh. Faculty of Electrical and Computer Engineering, Tarbiat Modares University. Iran. 1. Introduction.

Data Mining for Internet of Things: A Survey - Semantic Scholar

Engineering, National Chung Cheng University, 62102 Chia-Yi, Taiwan, ROC ...... [36] R. Roman, J. Zhou, and J. Lopez, â

Mining interesting knowledge from weblogs: a survey - Semantic Scholar

Data Mining which deals with the extraction of interesting knowledge from the World ..... SAS offers also an on-line Web analytic service, SAS IntelliVisor [90],.

Mining Intrusion Detection Alarms for Actionable ... - Semantic Scholar

occuring in a computer system. IDSs trigger alarms when they detect signs of security violations. The response to such security incidents is site-dependent, but ...

From Data Mining to Knowledge Mining - Semantic Scholar

In efforts to satisfy the growing need for new data analysis tools that can overcome the above ..... The instantiated model that best fits the data is then used.

Brief survey of crowdsourcing for data mining - Semantic Scholar

Jul 11, 2014 - tives to work on small tasks, the mining tasks can benefit from the aggregation of labeling .... is to di

A Brief survey of Data Mining Techniques Applied ... - Semantic Scholar

techniques and predictive modeling application in the agriculture field. Key words ... data analysis that can be used to extract models describing important data ...

Mining Intrusion Detection Alarms for Actionable Knowledge - CiteSeerX

cludes law suits, firewall reconfigurations, and the fixing of discovered ... republish, to post on servers or to redistribute to lists, requires prior specific permission and/or ..... The episodes and episode rules discovered contained many interest

A Recent Survey on Knowledge Discovery in Spatial Data Mining

www.IJCSI.org. 473. A Recent Survey on Knowledge Discovery in Spatial Data. Mining. Dr.M.Hemalatha1, N. Naga Saranya2. 1Head Dept of Software Systems,.

A Survey of Itemset Mining - Semantic Scholar

Liu, Y, Zhao, Y, Chen, L, Pei, J, Han, J. Mining frequent trajectory patterns for ac- ... Duan, Y, Fu, X, Luo, B, Wang, Z, Shi, J, Du, X. Detective: Automatically identify.

Association Rule Mining: A Survey - Semantic Scholar

mining techniques such as association rules, classifications and clustering. ...... giant amount of data poses a challenge of maintaining and updating the ...

A Theory of Actionable Data Mining with Application to Semiconductor ...

Ali, K. M., and Pazzani, M. J., "Error Reduction through Learning Multiple .... Tobin, K. W., Karnowski, T.P., and Lakhani, F., âA Survey of Semiconductor Data ...

Sports Knowledge Management and Data Mining - Semantic Scholar

Before the advent of data mining and knowledge management techniques, sports ... example is software used to monitor sports betting locales for unusual bets which may ... The impreciseness of sports statistics can be best illustrated by ...

knowledge management, data mining, and text ... - Semantic Scholar

management, data mining, and text mining techniques and their use in ... learning and data analysis including: probabilistic and statistical models, symbolic ...

geographic data mining and knowledge discovery - Semantic Scholar

Sep 21, 2000 - This chapter provides an introduction to geographic data mining and ..... warehousing and online geographic data repositories, the latter an ...

Dynamic knowledge mapping guided by data mining - Semantic Scholar

different data sources via the data mining technique in order to improve the ... the arrival of these two fields of application (Data Mining and Data Warehouse), a.

Knowledge Extraction by Visual Data Mining of ... - Semantic Scholar

subset of the existing geographical datasets for his particular planning task. It allows the ... analysis. The approach is implemented in the Visual Data Mining tool,.

Geographic Data Mining and Knowledge Discovery - Semantic Scholar

Nov 17, 2008 - Canosa (Apulia) and the feature extractor generates the description of all ...... Computing Science, Simon Fraser University, British Columbia, ...

Knowledge discovery and data mining in ... - Semantic Scholar

The new technologies for Knowledge Discovery from Databases (KDD) and data ... of data is thus available for the extraction of high-level information, including ...... Altschul, SF, Boguski, MS, Gish, W and Wootton, JC, 1994, ``Issues in ... networks

Spatial data mining and geographic knowledge ... - Semantic Scholar

data mining and geographic knowledge discovery has emerged as an active research .... however, the application of any data mining method should be car-.

Scalable Data Mining for Rules - Semantic Scholar

Pride and Prejudice also buy Emma within a month". Such patterns are ...... cessors/host) inter-connected by the Memory Channel Gillett, 1996] network. The.

Data Mining for GOR V3 - Semantic Scholar

combinations of feature pairs looking for successful application ... between C# and MySQL (available from http:// ... machine using Parallels Desktop for Mac.

Data Mining for Actionable Knowledge: A Survey - Semantic Scholar

Download PDF

18 downloads 122668 Views 134KB Size Report

Comment

There are two major issues in data mining research and applications: patterns and interest. The techniques of pattern discovering include classification, ...

!"

# #

.

$%& ' ( )*+ % /(

*+,,,* $% -% 0. % %

1

1

2

#

%& %

2 3

4 1

%6 1

1

#

1

3 ( 3

1 3

7 % 3 2 %

3

(

5 1

%

2 %

3 1

3 1 2 3

%

3

8 *6

2

!6

3 2% 9

3

% :

&

;

< 3

;

-

%

3

2 3

=

1

(

( %

3

%

>

8

=

1

% %& 3

2

%

1 3 1

(

?*+ *@A% 1 1 =

1 %

>

1

4

"

1

= =

1 4

(

#

3

1 4

1

3 23

!,,);;C !*D, B % !,,);;C*),!*6

%:

1 ': E- -

% 1

( =

%

# 1

9

$ %

5B %

3 &1

>

2

2

1

% 2 2

%

? % % * )4)FA

3 3

9

1 #

%

3 1

1

1 3

% 3 3

3

1

%

3 2

2 3 3 2

( 3 2

( 3 2% 1 3 8 *6

3 !6

3 2% 9

3 %

# )

38

3

!

4 1

C%9

3 2 3

2 3 3

1 3

+

2 3 = 1 3

% %

%

! ; G 1

>

1

2 3 >

1 ;

?*A% ;

2

3

2

3

% ?*A

%

3 1 ?*A% 9 3 = 1

3 6 = 1

2 1

1 1

5 3

%

3 %9

1

2 3 3 2 3

2 = 1 1

3

2%

1

3 1

2

1

% 1 2

E (

>

# $ 3

1

3

#

"

=

1

3

1 3

%

% 3 2 8 *6

2 3 3 2 !6 1

( 3 2%

% 4

2 %E

( %

3 2

( 3

% %

2

3

2 3

:

9 %*% (

2

#

;

< 3

2

2

3

3 2

; 3

2

1 % 3

3 2 2 9 %!%

3 2 1

3

3 1 ( 2 2 3 3 2 3 2 5 3 2 3 % 2 3 (

(

#

%

4

#

3 2 6

3 2 % 3 1

#

1

8 *6

!6 1 2%

4 1

:

3 2 1

(

)6

1

(

2

;B 3

:

;

&

#

#

3

< 3

$

2

2

3

3 2H;

&

$

'

!

3 1 3

&

8 *6

'

3 2

!

!6

3 2%

%

$

3

2

3

(

%

2 %

2

1

2

%I

1

2% :

2% 1 3

3 2%

3

1 3 2

%"

2 3 1 3

#

2 3

% &

( 2 >

5

6

%

%

3

%

3

7 %

2

1 2

4

%;

3 2

J

* ) ?!A% & K 3

(

2 J

3

2

K

%M

(

=

1

D,L 3

%

% 1

3 3 1

%

?@A% (

= 2

1

% 2

1

4

(

#

?)4*! A % 3

?)4@A%

3

% (

# %

3 %

4

(

%

4

% ?DA

%

1

4 3 (

#

3

% 7

2 1

?F A 3

3 % 3

7 (

% ;

1

3

% 3 % 3 1 2 %

2 M

1

1 (

% ?*,A

3 2

1

1 % M

% ?**A

4

4

# ?*!A 5&&;6 > 1

; 7 2 2

%

3

& >

%

7

>

1 4& (

4

1

>

1 4

2

%

& 3 % 3

(

3

3 (

1 %

3 4

#

1 3 2

%

-

5

2 2

6 4

% 3 1 3

3 (

6% & 63

?*)A 3

1 1

%'

( 3

4

%

3

3 ?*@A %

N

1 %-

(

1

#

(

5

1

?*+A (

8

?*CA%

5 2

(

M (

(

( % = 3

4 2

4 %:

(

( ; ?*) *CA

#

(

?*+ *@A

#

4

= 2 3

N

%

?*DA

= % %

4

% O

7

1

% 1

%9 1

4

3

2

1 3

4

% 1 3 4 2

3

'

?*FA

1

4

3 2

1 4

1

1

K

#

1

2 % J (

2

# %

3

1 3% 1 5 ;-6 % 3

3 3

2

3 '

M

%

* F ?* A%

= % % M

?!,A

%

;

3 4

&

1

%

" 3 %

1

38M %9

3

3 % &

1 3

?!*A% (

2

2

3 1

% ?!*A 1 3 2 1

%

?!*A % " %

;

% ?!! !)A 1 ( = ( ?!! !)A

( B9 1

3 2

1 %;

3

% ?!C A

# 1

?!CA

%

( 3

4

% 4

3

=

3

1 8

1

?!+A% 1

> >

1

%& 1

3 3

2

%:

1 4

1

1

3

% ?!@A %

= 3 ( 4 2 %

1

1

5 P

% ?!DA %

3 1 %: 1 =

6

3

2

1

2

1

2

2 1

= 3

-

M 4

%

?!@A

?!DA

1

3 2

3

%

$

#

2

% 2

?! A

?!FA%

3

3

(

> %" %

>

2 ?),A

1

1

1

(

%; # 3 >

2 ?)*A

2

2

1

-9:

1

4

% # 5% % 3 3 2%

3 3 1 2 &

1

2

1

6 3 ?!*A% 3

?)*A 2 1

%

&) 2 >

(

3

3 1

2

% 1

2

3 (

2 3 1 =

1

1

1 1

%

3

1

1

3

; 3 4=

1

J;

1 3

2 ?)!A%

32

1

%

1 K% "

3 1

%B

3

32

4&

%

3 1

( 2 3 ?))4)FA% ?))4)CA

3 (

3 2

?)@ )DA%

% 4

%

1

3 ?)FA

1

# -: 5 %

2 1

'

5

% 4

&

3

K 3

!

1

!

;

# $

) 3

( 3 2% 3 2

6

% 3 2

(

3

2 3 3 2 3

8

*

( 2% (

3

3 2 2

2 9

;

&

3 2 ?*,A ?**A

?!!4!FA ?),A

?*)4*DA

?))4)+A

?)4 A ?*!A

?!*A ?! A ?)*A

?*FA

?)@4)FA

3 2 3 2

9 5*6 9

*

1

#

4 3 2 4

3%

2 1 2

3 1

1

3 2

1

%&

4 3 2

2

2

% 5!6 = 6

3

1 3

5 =

#

%"

1

( 3 #

#

?) A% 1 3 2

=

% 3

#

5)6

%

3 2 1

2 1

=

4

%

1

3 2

%

2 3

=

1

3

%

* 3 ( 3 3 1 %

3 2 % 8 *6

1 3

2 3 =

2

3 2

(

!6

3 2%

2 3 >

3 1 3 2 3 = % 3 1

1

( 3 %

# #

3

%;

3 1

#

?C, C*A

3

3

%

( ?*A

;%

# ;%

#

%"

< ?!A

-% ;

?)A

3

%

%

8$

: 2

$

3

%

% :

?CA