data mining concepts and techniques by jiawei han micheline kamber notes
Data Mining Concepts and Techniques phần 1 potx
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... Reference Data in Enterprise Databases: Binding Corporate Data to the Wider World
Malcolm Chisholm
Data Mining: Concepts and Techniques
Jiawei Han and Micheline Kamber
Understanding SQL and Java ... Series in Data Management Systems
Series Editor: Jim Gray, Microsoft Research
Data Mining: Concepts and Techniques, Second Edition
Jiawei Han and Micheline Kamber
Querying XML: XQuery, XPath, and SQL/XML ... Foundations of Data Mining 665
11.3.2 Statistical Data Mining 666
11.3.3 Visual and Audio Data Mining 667
11.3.4 Data Mining and Collaborative Filtering 670
11.4 Social Impacts of Data Mining 675
11.4.1...
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Data Mining Concepts and Techniques phần 2 ppsx
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... inexpensive, can be applied to ordered and unordered
attributes, and can handle sparse data and skewed data. Multidimensional data
of more than two dimensions can be handled by reducing the problem to two
dimensions. ... 97
2.7
Summary
Data preprocessing is an important issue for both data warehousing and data mining,
as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessing
includes data cleaning, ... inaccurate data. So far, we have
looked at techniques for handling missing data and for smoothing data. “But data clean-
ing is a big job. What about data cleaning as a process? How exactly does...
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Data Mining Concepts and Techniques phần 3 docx
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... Chapter 3 Data Warehouse and OLAP Technology: An Overview
data by OLAP operations), and data mining (which supports knowledge discovery).
OLAP-based data mining is referred to as OLAP mining, or ... processing, and data
mining. We also introduce on-line analytical mining (OLAM), a powerful paradigm that
integrates OLAP with data mining technology.
3.5.1 Data Warehouse Usage
Data warehouses and data ... Warehouse and OLAP Technology: An Overview
3.5
From Data Warehousing to Data Mining
“How do data warehousing and OLAP relate to data mining? ” In this section, we study the
usage of data warehousing...
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Data Mining Concepts and Techniques phần 4 potx
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... sets of data. The attribute-oriented induction method
described in this chapter was first proposed by Cai, Cercone, and Han [CCH91] and
further extended by Han, Cai, and Cercone [HCC93], Han and Fu ... include data cube–based data aggregation and attribute-
oriented induction.
From a data analysis point of view, data generalization is a form of descriptive data
mining. Descriptive data mining ... techniques: data focusing, data generalization by
attribute removal or attribute generalization, count and aggregate value accumulation,
attribute generalization control, and generalization data...
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Oracle Data Guard Concepts and Administration
Ngày tải lên :
26/10/2013, 22:15
... Standby and Cascaded Remote Physical Standby C-5
C.3.2 Local Physical Standby and Cascaded Remote Logical Standby C-5
C.3.3 Local and Remote Physical Standby and Cascaded Local Logical Standby ... the redo data is applied to the standby database.
A standby database can be one of two types: a physical standby database or a
logical standby database. If needed, either type of standby database ... primary database. Once
the standby database is created and configured, Data Guard automatically
maintains the standby database by transmitting primary database redo data to the
standby system,...
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Oracle9i Data Mining Concepts Release 9.2.0.2 October 2002 Part No. A95961-02 Oracle9i Data
Ngày tải lên :
06/11/2013, 01:15
... Components
Oracle9i Data Mining has two main components:
■
Oracle9i Data Mining API
■
Data Mining Server (DMS)
1.2.1 Oracle9i Data Mining API
The Oracle9i Data Mining API is the component of Oracle9i Data Mining ... faster than the
viii
Basic ODM Concepts 1-1
1
Basic ODM Concepts
Oracle9i Data Mining (ODM) embeds data mining within the Oracle9i database.
The data never leaves the database — the data, data ... SQL/MM for Data Mining. JDM has also influenced these
standards. Oracle9i Data Mining will comply with the JDM standard when that
standard is published.
1.2.2 Data Mining Server
The Data Mining...
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Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining pdf
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15/03/2014, 09:20
... positive instances covered by both R0 and R1
p0: number of positive instances covered by R0
n0: number of negative instances covered by R0
p1: number of positive instances covered by R1
n1: number of ... negative instances covered by R1
© Tan,Steinbach, Kumar Introduction to Data Mining 36
Instance Based Classifiers
Examples:
Rote-learner
ã
Memorizes entire training data and performs
classification ... to $1M
© Tan,Steinbach, Kumar Introduction to Data Mining 40
1 nearest-neighbor
Voronoi Diagram
© Tan,Steinbach, Kumar Introduction to Data Mining 38
Nearest-Neighbor Classifiers
Requires...
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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
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... Married 80K
?
10
Test Data
© Tan,Steinbach, Kumar Introduction to Data Mining 25
Splitting Based on Continuous Attributes
© Tan,Steinbach, Kumar Introduction to Data Mining 46
Tree Induction
Greedy ... Tan,Steinbach, Kumar Introduction to Data Mining 3
Illustrating Classification Task
© Tan,Steinbach, Kumar Introduction to Data Mining 13
Apply Model to Test Data
Refund
MarSt
TaxInc
YES
NO
NO
NO
Yes ... Attributes
Training Data
Model: Decision Tree
© Tan,Steinbach, Kumar Introduction to Data Mining 8
Decision Tree Classification Task
Decision
Tree
© Tan,Steinbach, Kumar Introduction to Data Mining 35...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
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15/03/2014, 09:20
... Specific-to-general
Data Mining
Association Analysis: Basic Concepts
and Algorithms
Lecture Notes for Chapter 6
Introduction to Data Mining
by
Tan, Steinbach, Kumar
â Tan,Steinbach, Kumar Introduction to Data ... to Data Mining 49
Effect of Support Distribution
Many real data sets have skewed support distribution
Support
distribution of
a retail data set
© Tan,Steinbach, Kumar Introduction to Data Mining ... may decouple the support and confidence requirements
© Tan,Steinbach, Kumar Introduction to Data Mining 16
Reducing Number of Comparisons
Candidate counting:
–
Scan the database of transactions...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
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15/03/2014, 09:20
... Kumar Introduction to Data Mining 4
Handling Categorical Attributes
Potential Issues
What if attribute has many possible values
ã
Example: attribute country has more than 200 possible values
ã
... pass over the sequence database D to find the support
for these candidate sequences
Candidate Elimination:
ã
Eliminate candidate k-sequences whose actual support is less than
minsup
© Tan,Steinbach, ... à > à +
ã
Z has zero mean and variance 1 under null hypothesis
2
2
2
1
2
1
'
n
s
n
s
Z
+
=
àà
© Tan,Steinbach, Kumar Introduction to Data Mining 3
Handling Categorical Attributes
Transform...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
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15/03/2014, 09:20
... Introduction to Data Mining 5
Notion of a Cluster can be Ambiguous
How many clusters?
Four Clusters Two Clusters
Six Clusters
© Tan,Steinbach, Kumar Introduction to Data Mining 41
Limitations ... Tan,Steinbach, Kumar Introduction to Data Mining 42
Limitations of K-means: Non-globular Shapes
Original Points
K-means (2 Clusters)
© Tan,Steinbach, Kumar Introduction to Data Mining 13
Types of Clusters: ... outliers are present.
6 density-based clusters
© Tan,Steinbach, Kumar Introduction to Data Mining 34
Handling Empty Clusters
Basic K-means algorithm can yield empty clusters
Several strategies
–
Choose...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
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... Tan,Steinbach, Kumar Introduction to Data Mining 22
Experimental Results: CHAMELEON
© Tan,Steinbach, Kumar Introduction to Data Mining 7
CURE Cannot Handle Differing Densities
Original Points
CURE
... © Tan,Steinbach, Kumar Introduction to Data Mining 35
SNN Clustering Can Handle Other Difficult Situations
© Tan,Steinbach, Kumar Introduction to Data Mining 24
Experimental Results: CURE (15 ... Can Handle Differing Densities
Original Points
SNN Clustering
© Tan,Steinbach, Kumar Introduction to Data Mining 13
Limitations of Current Merging Schemes
Closeness schemes
will merge (a) and...
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