Association rule mining: models and algorithms
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London
Springer
2002
|
| Series: | Lecture notes in computer science
2307 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XII, 238 S. |
| ISBN: | 3540435336 |
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MARC
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| 100 | 1 | |a Zhang, Chengqi |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Association rule mining |b models and algorithms |c Chengqi Zhang ; Shichao Zhang |
| 264 | 1 | |a Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London |b Springer |c 2002 | |
| 300 | |a XII, 238 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Lecture notes in computer science |v 2307 | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Rule-based programming | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
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Record in the Search Index
| _version_ | 1820874910634868736 |
|---|---|
| adam_text |
CONTENTS
1.
INTRODUCTION
.
1
1
.1
WHA
TI
SDA
TAMINING
?
.
1
1
.2
WHYDOWENE
E
DDA
TAMINING
?
.
2
1
.3
K
NOW
LE
DG
EDIS
C
OVE
R
YINDA
TA
BA
S
E
S(K
DD).
4
1
.3
.1
PR
O
C
E
S
S
INGSTE
PSO
FK
DD.
4
1
.3
.2
FE
A
TUR
ESE
LE
C
TIO
N.
6
1.3.3
APPLICATIONS
OF
KNOWLEDGE
DISCOVERY
IN
DATABASES.
.
.
.
7
1
.4
DA
TAMININGTA
S
K
.
7
1
.5
DA
TAMININGTE
CHNIQ
UE
S.
9
1
.5
.1
C
LUS
TE
R
ING.
9
1
.5
.2
C
LA
S
S
IFIC
A
TIO
N
.
1
0
1.5.3
CONCEPTUAL
CLUSTERING
AND
CLASSIFICATION
.
.
.
.
.
.
.
.
.
.
.
.
14
1
.5
.4
DE
P
E
NDE
NC
YMO
DE
LING
.
1
5
1
.5
.5
SUMMA
R
IZ
A
TIO
N.
1
5
1
.5
.6
RE
G
R
E
S
S
IO
N.
1
6
1
.5
.7
C
A
S
E
-
B
A
S
E
DL
E
A
R
NING.
1
6
1
.5
.8
MININGT
IME
-
SE
R
IE
SDA
TA.
1
7
1
.6
DA
TAMININGA
NDMA
R
KE
TING
.
1
7
1.7
SOLVING
REAL-WORLD
PROBLEMS
BY
DATA
MINING
.
.
.
.
.
.
.
.
.
.
.
.
.
.
18
1
.8
SUMMA
R
Y.
2
1
1
.8
.1
TR
E
NDSO
FDA
TAMINING.
2
1
1
.8
.2
O
UTLINE
.
2
2
2.
ASSOCIATION
RULE
.
25
2
.1
B
A
S
ICC
O
NC
E
PTS.
2
5
2
.2
ME
A
S
UR
E
ME
NTO
FAS
S
O
C
IA
TIO
NRULE
S.
3
0
2.2.1
SUPPORT-CONFIDENCE
FRAMEWORK
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
30
2
.2
.2
T
HR
E
EE
S
TA
BLIS
HE
DME
A
S
UR
E
ME
NTS.
3
1
2
.3
SE
A
R
CHINGFR
E
Q
UE
NTI
TE
MS
E
TS
.
3
3
2
.3
.1
T
HEAPR
IO
R
IALG
O
R
ITHM.
3
3
2
.3
.2
I
DE
NTIFY
INGI
TE
MS
E
TSO
FI
NTE
R
E
S
T.
3
6
2
.4
RE
S
E
A
R
CHINTOMININGAS
S
O
C
IA
TIO
NRULE
S
.
3
9
2
.4
.1
C
HI-
S
Q
UA
R
E
DTE
S
TME
THO
D.
4
0
2
.4
.2
T
HEFP-
TR
E
EB
A
S
E
DMO
DE
L
.
4
3
XC
O
N
T
E
N
T
S
2
.4
.3
O
PUSB
A
S
E
DALG
O
R
ITHM.
4
4
2
.5
SUMMA
R
Y.
4
6
3.
NEGATIVE
ASSOCIATION
RULE
.
47
3
.1
I
NTR
O
DUC
TIO
N
.
4
7
3
.2
FO
C
US
INGO
NI
TE
MS
E
TSO
FI
NTE
R
E
S
T.
5
1
3.3
EFFECTIVENESS
OF
FOCUSING
ON
INFREQUENT
ITEMSETS
OF
INTEREST
.
.
53
3
.4
I
TE
MS
E
TSO
FI
NTE
R
E
S
T
.
5
5
3
.4
.1
PO
S
ITIVEI
TE
MS
E
TSO
FI
NTE
R
E
S
T
.
5
5
3
.4
.2
NE
G
A
TIVEI
TE
MS
E
TSO
FI
NTE
R
E
S
T.
5
8
3
.5
SE
A
R
CHINGI
NTE
R
E
S
TINGI
TE
MS
E
TS.
5
9
3
.5
.1
PR
O
C
E
DUR
E.
5
9
3
.5
.2
ANE
X
A
MPLE.
6
2
3
.5
.3
AT
W
IC
E
-
PR
UNINGAPPR
O
A
CH.
6
5
3
.6
NE
G
A
TIVEAS
S
O
C
IA
TIO
NRULE
SO
FI
NTE
R
E
S
T.
6
6
3
.6
.1
ME
A
S
UR
E
ME
NT
.
6
6
3
.6
.2
E
X
A
MPLE
S
.
7
1
3
.7
ALG
O
R
ITHMSDE
S
IG
N.
7
3
3
.8
I
DE
NTIFY
INGRE
LIA
BLEE
X
C
E
PTIO
NS.
7
5
3.8.1
CONFIDENCE
BASED
INTERESTINGNESS
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
75
3.8.2
SUPPORT
BASED
INTERESTINGNESS
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
77
3
.8
.3
SE
A
R
CHINGRE
LIA
BLEE
X
C
E
PTIO
NS.
7
8
3
.9
C
O
MPA
R
IS
O
NS.
8
0
3.9.1
COMPARISON
WITH
SUPPORT-CONFIDENCE
FRAMEWORK
.
.
.
.
.
80
3
.9
.2
C
O
MPA
R
IS
O
NW
ITHI
NTE
R
E
S
TMO
DE
LS.
8
0
3
.9
.3
C
O
MPA
R
IS
O
NW
ITHE
X
C
E
PTIO
NMININGMO
DE
L.
8
1
3.9.4
COMPARISON
WITH
STRONG
NEGATIVE
ASSOCIATION
MODEL
.
82
3
.1
0
SUMMA
R
Y.
8
3
4.
CAUSALITY
IN
DATABASES
.
85
4
.1
I
NTR
O
DUC
TIO
N
.
8
5
4
.2
B
A
S
ICDE
FINITIO
NS
.
8
7
4
.3
DA
TAPA
R
TITIO
NING
.
9
0
4.3.1
PARTITIONING
DOMAINS
OF
ATTRIBUTES
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
90
4
.3
.2
Q
UA
NTITA
TIVEI
TE
MS
.
9
2
4.3.3
DECOMPOSITION
AND
COMPOSITION
OF
QUANTITATIVE
ITEMS
93
4
.3
.4
I
TE
MVA
R
IA
BLE
S
.
9
5
4.3.5
DECOMPOSITION
AND
COMPOSITION
FOR
ITEM
VARIABLES
.
.
.
96
4
.3
.6
PR
O
C
E
DUR
EO
FPA
R
TITIO
NING.
9
8
4
.4
DE
P
E
NDE
NC
YA
MO
NGVA
R
IA
BLE
S
.
9
9
4.4.1
CONDITIONAL
PROBABILITIES
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
100
4
.4
.2
C
A
US
A
LRULE
SO
FI
NTE
R
E
S
T.
1
0
1
4
.4
.3
ALG
O
R
ITHMDE
S
IG
N
.
1
0
3
4.5
CAUSALITY
IN
PROBABILISTIC
DATABASES
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
105
4
.5
.1
PR
O
BLE
MSTA
TE
ME
NT.
1
0
5
CONTENTS
XI
4
.5
.2
RE
Q
UIR
E
DC
O
NC
E
PTS
.
1
0
8
4
.5
.3
PR
E
PR
O
C
E
S
SO
FDA
TA
.
1
0
8
4.5.4
PROBABILISTIC
DEPENDENCY.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
110
4
.5
.5
I
MPR
OVE
ME
NTS.
1
1
5
4
.6
SUMMA
R
Y.
1
1
9
5.
CAUSAL
RULE
ANALYSIS
.
121
5
.1
I
NTR
O
DUC
TIO
N
.
1
2
1
5
.2
PR
O
BLE
MSTA
TE
ME
NT
.
1
2
2
5
.2
.1
RE
LA
TE
DC
O
NC
E
PTS.
1
2
4
5
.3
O
PTIMIZ
INGC
A
US
A
LRULE
S
.
1
2
6
5
.3
.1
UNNE
C
E
S
S
A
R
YI
NFO
R
MA
TIO
N
.
1
2
6
5
.3
.2
ME
R
G
INGUNNE
C
E
S
S
A
R
YI
NFO
R
MA
TIO
N
.
1
2
7
5
.3
.3
ME
R
G
INGI
TE
MSW
ITHI
DE
NTIC
A
LPR
O
P
E
R
TIE
S.
1
3
0
5
.4
PO
LY
NO
MIA
LFUNC
TIO
NFO
RC
A
US
A
LITY.
1
3
1
5
.4
.1
C
A
US
A
LRE
LA
TIO
NS
HIP
.
1
3
2
5
.4
.2
B
INA
R
YL
INE
A
RC
A
US
A
LIT
Y.
1
3
2
5.4.3
N-ARY
LINEAR
PROPAGATING
MODEL
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
137
5
.4
.4
E
X
A
MPLE
S
.
1
3
9
5
.5
FUNC
TIO
NSFO
RGE
NE
R
A
LC
A
US
A
LITY
.
1
4
3
5
.6
APPR
OX
IMA
TINGC
A
US
A
LIT
YBYFITTING
.
1
4
9
5
.6
.1
PR
E
PR
O
C
E
S
S
INGO
FDA
TA.
1
4
9
5.6.2
CONSTRUCTING
THE
POLYNOMIAL
FUNCTION
.
.
.
.
.
.
.
.
.
.
.
.
.
.
150
5
.6
.3
ALG
O
R
ITHMDE
S
IG
N
.
1
5
5
5
.6
.4
E
X
A
MPLE
S
.
1
5
6
5
.7
SUMMA
R
Y.
1
5
9
6.
ASSOCIATION
RULES
IN
VERY
LARGE
DATABASES
.
161
6
.1
I
NTR
O
DUC
TIO
N
.
1
6
1
6
.2
I
NS
TA
NC
ESE
LE
C
TIO
N
.
1
6
4
6
.2
.1
E
VA
LUA
TINGTHESIZ
EO
FI
NS
TA
NC
ESE
TS
.
1
6
4
6
.2
.2
GE
NE
R
A
TINGI
NS
TA
NC
ESE
T.
1
6
7
6
.3
E
S
TIMA
TIO
NO
FAS
S
O
C
IA
TIO
NRULE
S.
1
6
9
6.3.1
IDENTIFYING
APPROXIMATE
FREQUENT
ITEMSETS
.
.
.
.
.
.
.
.
.
.
169
6.3.2
MEASURING
ASSOCIATION
RULES
OF
INTEREST
.
.
.
.
.
.
.
.
.
.
.
.
.
171
6
.3
.3
ALG
O
R
ITHMDE
S
IG
NING.
1
7
2
6.4
SEARCHING
TRUE
ASSOCIATION
RULES
BASED
ON
APPROXIMATIONS
.
173
6
.5
I
NC
R
E
ME
NTA
LMINING.
1
7
9
6
.5
.1
PR
O
MIS
INGI
TE
MS
E
TS
.
1
8
0
6
.5
.2
SE
A
R
CHINGPR
O
C
E
DUR
E.
1
8
2
6
.5
.3
C
O
MP
E
TITIVESE
TME
THO
D
.
1
8
7
6
.5
.4
AS
S
IG
NINGWE
IG
HTS.
1
8
8
6
.5
.5
ALG
O
R
ITHMO
FI
NC
R
E
ME
NTA
LMINING.
1
9
0
6
.6
I
MPR
OVE
ME
NTO
FI
NC
R
E
ME
NTA
LMINING
.
1
9
3
6
.6
.1
C
O
NDITIO
NSO
FTE
R
MINA
TIO
N.
1
9
3
XII
CONTENTS
6
.6
.2
ANY
TIMESE
A
R
CHALG
O
R
ITHM
.
1
9
4
6
.7
SUMMA
R
Y.
1
9
7
7.
ASSOCIATION
RULES
IN
SMALL
DATABASES
.
199
7
.1
I
NTR
O
DUC
TIO
N
.
2
0
0
7
.2
PR
O
BLE
MSTA
TE
ME
NT
.
2
0
1
7.2.1
PROBLEMS
FACED
BY
UTILIZING
EXTERNAL
DATA
.
.
.
.
.
.
.
.
.
.
201
7
.2
.2
O
URAPPR
O
A
CH
.
2
0
3
7
.3
E
X
TE
R
NA
LDA
TAC
O
LLE
C
TING
.
2
0
4
7
.3
.1
AVA
ILA
BLETO
O
LS
.
2
0
4
7.3.2
INDEXING
BY
A
CONDITIONAL
ASSOCIATED
SEMANTIC
.
.
.
.
.
.
206
7
.3
.3
PR
O
C
E
DUR
E
SFO
RSIMILA
R
ITY.
2
0
8
7
.4
ADA
TAPR
E
PR
O
C
E
S
S
INGFR
A
ME
WO
R
K
.
2
0
9
7.4.1
PRE-ANALYSIS:
SELECTING
RELEVANT
AND
UNCONTRADICTABLE
C
O
LLE
C
TE
DDA
TA
-
SO
UR
C
E
S.
2
0
9
7.4.2
POST-ANALYSIS:
SUMMARIZING
HISTORICAL
DATA
.
.
.
.
.
.
.
.
.
.
212
7
.4
.3
ALG
O
R
ITHMDE
S
IG
NING.
2
1
4
7
.5
SY
NTHE
S
IZ
INGSE
LE
C
TE
DRULE
S.
2
1
7
7
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E
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2
2
2
7
.7
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2
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CONCLUSION
AND
FUTURE
WORK
.
225
8
.1
C
O
NC
LUS
IO
N
.
2
2
5
8
.2
FUTUR
EWO
R
K.
2
2
6
REFERENCES
.
229
SUBJECT
INDEX
.
237 |
| any_adam_object | 1 |
| author | Zhang, Chengqi Zhang, Shichao |
| author_GND | (DE-588)123637260 |
| author_facet | Zhang, Chengqi Zhang, Shichao |
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| author_variant | c z cz s z sz |
| building | Verbundindex |
| bvnumber | BV014221470 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.9.D343 |
| callnumber-search | QA76.9.D343 |
| callnumber-sort | QA 276.9 D343 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SS 4800 |
| classification_tum | DAT 708f |
| ctrlnum | (OCoLC)50192406 (DE-599)BVBBV014221470 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV014221470 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-10T15:07:15Z |
| institution | BVB |
| isbn | 3540435336 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009751333 |
| oclc_num | 50192406 |
| open_access_boolean | |
| owner | DE-384 DE-91G DE-BY-TUM DE-739 DE-824 DE-706 DE-83 DE-11 DE-1049 |
| owner_facet | DE-384 DE-91G DE-BY-TUM DE-739 DE-824 DE-706 DE-83 DE-11 DE-1049 |
| physical | XII, 238 S. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Springer |
| record_format | marc |
| series | Lecture notes in computer science |
| series2 | Lecture notes in computer science |
| spelling | Zhang, Chengqi Verfasser aut Association rule mining models and algorithms Chengqi Zhang ; Shichao Zhang Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London Springer 2002 XII, 238 S. txt rdacontent n rdamedia nc rdacarrier Lecture notes in computer science 2307 Computer algorithms Data mining Rule-based programming Data Mining (DE-588)4428654-5 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Produktionsregelsystem (DE-588)4258171-0 gnd rswk-swf Data Mining (DE-588)4428654-5 s Produktionsregelsystem (DE-588)4258171-0 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Wissensextraktion (DE-588)4546354-2 s Zhang, Shichao Verfasser (DE-588)123637260 aut Lecture notes in computer science 2307 (DE-604)BV000000607 2307 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009751333&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhang, Chengqi Zhang, Shichao Association rule mining models and algorithms Lecture notes in computer science Computer algorithms Data mining Rule-based programming Data Mining (DE-588)4428654-5 gnd Wissensextraktion (DE-588)4546354-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Produktionsregelsystem (DE-588)4258171-0 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4546354-2 (DE-588)4193754-5 (DE-588)4258171-0 |
| title | Association rule mining models and algorithms |
| title_auth | Association rule mining models and algorithms |
| title_exact_search | Association rule mining models and algorithms |
| title_full | Association rule mining models and algorithms Chengqi Zhang ; Shichao Zhang |
| title_fullStr | Association rule mining models and algorithms Chengqi Zhang ; Shichao Zhang |
| title_full_unstemmed | Association rule mining models and algorithms Chengqi Zhang ; Shichao Zhang |
| title_short | Association rule mining |
| title_sort | association rule mining models and algorithms |
| title_sub | models and algorithms |
| topic | Computer algorithms Data mining Rule-based programming Data Mining (DE-588)4428654-5 gnd Wissensextraktion (DE-588)4546354-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Produktionsregelsystem (DE-588)4258171-0 gnd |
| topic_facet | Computer algorithms Data mining Rule-based programming Data Mining Wissensextraktion Maschinelles Lernen Produktionsregelsystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009751333&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000000607 |
| work_keys_str_mv | AT zhangchengqi associationruleminingmodelsandalgorithms AT zhangshichao associationruleminingmodelsandalgorithms |