a survey of data mining and knowledge discovery process models and methodologies
Web Mining and Knowledge Discovery of Usage Patterns
Ngày tải lên :
31/08/2012, 16:46
... authors of [5] classified the data
type as content data, structure data, usage data, and user profile data. M. Spiliopoulou [14]
categorized the Web mining into Web usage mining, Web text mining ... and analyze the useful information from the
Web data. The authors of [10] claims the Web involves three types of data: data on the Web
(content), Web log data (usage) and Web structure data. ... Zaiane, M. Xin, J. Han. Discovering Web Access Patterns and Trends by applying
OLAP and Data Mining Technology on Web Logs. In Advances in Digital Libraries, pages
19-29, Santa Barbara, CA,...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 130 doc
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... 1189
Data cleaning, 19, 615
Data collection, 1084
Data envelop analysis (DEA), 968
Data management, 559
Data mining, 1082
Data Mining Tools, 1155
Data reduction, 126, 349, 554, 566, 615
Data transformation, ... Mining, and many data visualization facilities and data
preprocessing tools are provided. All algorithms and methods take their input in the form of a
single relational table, which can be read from ... graphically through
visualization of the data and examination of the model (if the model structure is amenable to
visualization). Users can also load and save models.
Eibe Frank et al.
66 Weka -A Machine...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 1 pps
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... edition. Ad-
vances occurred in areas, such as Multimedia Data Mining, Data Stream Mining,
Spatio-temporal Data Mining, Sequences Analysis, Swarm Intelligence, Multi-label
classification and privacy ... in Data Mining, such
as statistical methods for Data Mining, logics for Data Mining, DM query languages,
text mining, web mining, causal discovery, ensemble methods, and a great deal more.
Part ... identifying valid,
novel, useful, and understandable patterns from large datasets. Data Mining (DM)
is the mathematical core of the KDD process, involving the inferring algorithms
that explore the data, ...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 2 pptx
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04/07/2014, 05:21
... Mining
58 Data Mining in Medicine
Nada Lavra
ˇ
c, Bla
ˇ
z Zupan 1111
59 Learning Information Patterns in Biological Databases - Stochastic
Data Mining
Gautam B. Singh 1137
60 Data Mining for Financial ... Barko 1041
56 Mining Time Series Data
Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos,
Eamonn
Keogh, Michail Vlachos, Gautam Das 1049
Part VII Applications
57 Multimedia Data Mining
58 ... Pfahringer
Department of Computer Science,
University of Waikato, New Zealand
Marco F. Ramoni
Departments of Pediatrics and Medicine
Harvard University, USA
Chotirat Ann Ratanamahatana
Department...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 3 pptx
Ngày tải lên :
04/07/2014, 05:21
... unknown
patterns. The model is used for understanding phenomena from the data, analysis
and prediction.
The accessibility and abundance of data today makes Knowledge Discovery and
Data Mining a matter ... Knowledge Discovery and Data Mining 3
Fig. 1.1. The Process of Knowledge Discovery in Databases.
be determined. This includes finding out what data is available, obtaining
additional necessary data, and ... best
understanding the phenomena. This tradeoff represents an aspect where the
interactive and iterative aspect of the KDD is taking place. It starts with the
best available data set and later expands and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 4 ppsx
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04/07/2014, 05:21
... Multimedia Data Mining (Chapter 57). Multimedia data mining, as
the name suggests, presumably is a combination of the two emerging areas: mul-
timedia and data mining. Instead, the multimedia data mining ... I.H. and Frank, E., Data Mining: Practical machine learning tools and techniques,
Morgan Kaufmann Pub, 2005.
Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and
McLachlan, ... such data is that it is unbounded in
terms of continuity of data generation. This form of data has been termed as data
streams to express its owing nature. Mohamed Medhat Gaber, Arkady Zaslavsky,
and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 5 pptx
Ngày tải lên :
04/07/2014, 05:21
... The major areas
that include data cleansing as part of their defining processes are: data warehousing,
knowledge discovery in databases, and data/ information quality management (e.g.,
Total Data ... investigate such very large data sets has
given rise to the fields of Data Mining (DM) and data warehousing (DW). Without
clean and correct data the usefulness of Data Mining and data warehousing ... (Ballou and Tayi, 1999, Redman, 1998, Wang et al., 2001) and
some tools exist to assist in manual data cleansing and/ or relational data integrity
analysis.
The serious need to store, analyze, and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 6 ppt
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... 464-467.
Brachman, R. J., Anand, T., The Process of Knowledge Discovery in Databases — A
Human–Centered Approach. In Advances in Knowledge Discovery and Data Min-
ing, Fayyad, U. M., Piatetsky-Shapiro, ... Information Patterns and Data Cleaning. In
Advances in Knowledge Discovery and Data Mining, Fayyad, U. M., Piatetsky-Shapiro,
G., Smyth, P., & Uthurasamy, R., eds. MIT Press/AAAI Press, 1996.
Hamming, ... Very Large Data Bases; 1998 New
York. 392-403.
32 Jonathan I. Maletic and Andrian Marcus
Wang, R., Storey, V., & Firth, C. A Framework for Analysis of Data Quality Research, IEEE
Transactions...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 7 ppsx
Ngày tải lên :
04/07/2014, 05:21
... identified
as a chase algorithm, was also discussed in (Dardzinska and Ras, 200 3A, Dardzinska
and Ras, 2003B).
Learning missing attribute values from summary constraints was reported in (Wu
and Barbara, ... is a Monte Carlo method of handling missing
attribute values in which missing attribute values are replaced by many plausible
values, then many complete data sets are analyzed and the results are ... 2002,Wu and Barbara, 2002). Yet another approach to handling missing
attribute values was presented in (Greco et al., 2000).
There is a number of statistical methods of handling missing attribute values,
usually...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 8 potx
Ngày tải lên :
04/07/2014, 05:21
... Programs for Machine Learning. Morgan Kaufmann Publishers, San
Mateo CA (1993).
Schafer J.L. Analysis of Incomplete Multivariate Data. Chapman and Hall, London, 1997.
Slowinski R. and Vanderpooten ... variance of the data along the direction n.
To characterize the remaining variance of the data, let’s find that direction m which
is both orthogonal to n, and along which the projected data again ... the {e
a
} span the space, we can expand n in terms of them: n =
∑
d
a= 1
α
a
e
a
,
and we’d like to find the
α
a
that maximize n
Cn = n
∑
a
α
a
Ce
a
=
∑
a
λ
a
α
2
a
, subject
to
∑
a
α
2
a
= 1...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 9 pdf
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04/07/2014, 05:21
... between each pair of data points in the dataset
(note that this measure can be very general, and in particular can allow for non-
vectorial data) . Given this, MDS searches for a mapping of the (possibly ... for audio or
video data) and to make the features more robust. The above features, computed by
taking projections along the n’s, are first translated and normalized so that the signal
data has ... (Basilevsky,
1994, Tipping and Bishop, 199 9A) . Suppose that
Ψ
=
σ
2
1, that the d −d
smallest
eigenvalues of the model covariance are the same and are equal to
σ
2
, and that the
sample covariance...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 10 ppt
Ngày tải lên :
04/07/2014, 05:21
... Linear Embedding
Locally linear embedding (LLE) (Roweis and Saul, 2000) models the manifold by
treating it as a union of linear patches, in analogy to using coordinate charts to pa-
rameterize a ... defined as
the sum of the weights of the removed arcs. Given the mapping of data to graph de-
fined above, a cut defines a split of the data into two clusters, and the minimum cut
encapsulates the ... S
d
is of full rank. This can
be seen as follows: since the rank of Z is d
and since the rank of a product of matri-
ces is bounded above by the rank of each, we have that d
= rank(Z)=rank(Y
PY...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 11 pdf
Ngày tải lên :
04/07/2014, 05:21
... Reduction and Feature Selection
Barak Chizi
1
and Oded Maimon
1
Tel-Aviv University
Summary. Data Mining algorithms search for meaningful patterns in raw data sets. The Data
Mining process requires ... the dimensionality of the data, it holds out the possibility of more effective
& rapid operation of data mining algorithms (i.e. Data Mining algorithms can be
operated faster and more effectively ... using
cross- validation (a wrapper approach) to estimate the accuracy of tables (and hence
feature sets). The MDL approach was shown to be more efficient than, and perform
as well as, as cross- validation.
An...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 12 ppsx
Ngày tải lên :
04/07/2014, 05:21
... a survey of variable selection. Suppose is Y a variable
of interest, and X
1
, ,X
p
is a set of potential explanatory variables or predictors,
are vectors of n observations. The problem of variable ... 97
5.3.4 Factor Analysis (FA)
Like PCA, factor analysis (FA) is also a linear method, based on the second-order
data summaries. First suggested by psychologists, FA assumes that the measured
variables ... using a search strategy and
cross validation to estimate accuracy. For each instance in back-ward the training
set, RC finds its nearest neighbour of the same class and removes those features...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 13 pot
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... value range of the quantitative data. It then as-
sociates a qualitative value to each interval. A cut point is a value among the quanti-
tative data where an interval boundary is located by a ... Time-insensitive
discretization only uses the stationary pro-perties of the quantitative data.
9. Ordinal vs. Nominal. Ordinal discretization transforms quantitative data into
ordinal qualitative data. It aims at taking ... referred to as categorical data, are data that can be
placed into distinct categories. Qualitative data sometimes can be arrayed in a mean-
ingful order. But no arithmetic operations can be applied...
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