Data Mining and Knowledge Discovery Handbook, 2 Edition part 36 pps
Data Mining and Knowledge Discovery Handbook, 2 Edition part 36 pps
... on Knowledge Discovery and Data
Mining. ACM Press.
Zaki, M. (20 00). Scalable algorithms for association mining. IEEE Transactions on Knowl-
edge and Data Engineering, 12( 3):3 72 390.
Zaki, M. and ... since he/she specifies the data and/ or pat-
terns of interests.
O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed.,
DOI 10.1007/9...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 1 pps
... Rokach
Editors
Data Mining and Knowledge
Discovery Handbook
Second Edition
123
Contents
1 Introduction to Knowledge Discovery and Data Mining
Oded Maimon, Lior Rokach 1
Part I Preprocessing Methods
2 Data ... neural networks, and evolutionary algorithms.
Parts five and six present supporting and advanced methods in Data Mining, such
as statistical methods...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 4 ppsx
... in data mining, Data Mining and Knowledge Discovery, 15(1):87-97, 20 07.
Larose, D.T., Discovering knowledge in data: an introduction to data mining, John Wiley and
Sons, 20 05.
Maimon O., and ... Pub, 20 05.
Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and
McLachlan, G.J. and Ng, A. and Liu, B. and Yu, P.S. an...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 7 ppsx
... Foundations and New Directions in Data Mining, as-
sociated with the third IEEE International Conference on Data Mining, Melbourne, FL,
November 1 922 , 24 –30, 20 03A.
Dardzinska A. and Ras Z.W. On rule discovery ... (Dardzinska and Ras, 20 03A,Dardzinska
and Ras, 20 03B).
Learning missing attribute values from summary constraints was reported in (Wu
and Barbara, 20 02...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 12 ppsx
... Springer, pp. 178-196, 20 02.
Maimon, O. and Rokach, L., Decomposition Methodology for Knowledge Discovery and
Data Mining: Theory and Applications, Series in Machine Perception and Artificial In-
telligence ... Kaufmann, 1996.
Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors
manufacturing case study, in Data Mining for Design and...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 16 ppsx
... Conference on Data- mining
(ICDM’ 02) , Maebashi City, Japan, CSIRO Technical Report CMIS- 02/ 1 02, 20 02.
Williams G. J., Huang Z., Mining the knowledge mine: The hot spots methodology for
mining large ... phenomena).
When data is limited, it is common practice to re-sample the data, that is, partition
the data into training and test sets in different ways. An inducer is tr...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 17 ppsx
... 131–158.
Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery
Handbook, pp. 321 –3 52, 20 05, Springer.
Rokach, L. and Maimon, O., Data mining for improving the quality of manufacturing: ... 14: 2, 24 1-301, 20 02.
Shafer, J. C., Agrawal, R. and Mehta, M. , SPRINT: A Scalable Parallel Classifier for Data
Mining, Proc. 22 nd Int. Conf...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 22 pps
... (Sebastiani and Ramoni,
20 00, Sebastiani and Ramoni, 20 01B) to customer profiling (Sebastiani et al., 20 00)
and bioinformatics (Friedman, 20 04,Sebastiani et al., 20 04 ,2) . Here we describe two
Data Mining ... =
∑
i
{log(
σ
2
i0
/
σ
2
i0
) −(y
i
−
μ
i1
)
2
/
σ
2
i1
+(y
i
−
μ
i0
)
2
/
σ
2
i0
}
where y
i
is the value of attribute i in the new sample to classify a...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 30 ppsx
... as:
d(x
i
,x
j
)=(w
1
x
i1
−x
j1
g
+ w
2
x
i2
−x
j2
g
+ +w
p
x
ip
−x
jp
g
)
1/g
where w
i
∈ [0,∞)
27 8 Lior Rokach
can be interpreted as agreements, and b and c as disagreements. The Rand index is
defined as:
RAND ... + b + c + d
The Rand index lies between 0 and 1. When the two partitions agree perfectly, the
Rand index is 1.
A problem with the Rand index is t...
Data Mining and Knowledge Discovery Handbook, 2 Edition part 31 pps
... (Farley and Raftery, 1998): an “E-step”, in which the
conditional expectation of the complete data likelihood given the observed data and
the current parameter estimates is computed, and an “M-step”, ... Bernoulli, Poisson, and log-normal distributions (Cheese-
man and Stutz, 1996). Other well-known density-based methods include: SNOB
(Wallace and Dowe, 1994) and MCLUST (Far...
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