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PDF generated at: Mon, 14 Jul 2014 06:58:30 UTC
Data Mining Algorithms In R
Contents
Articles
Data Mining Algorithms In R 1
Dimensionality Reduction 2
Frequent Pattern Mining 2
Sequence Mining 2
Clustering 3
Classification 3
R Packages 4
Principal Component Analysis 4
Singular Value Decomposition 10
Feature Selection 16
The Eclat Algorithm 21
arulesNBMiner 27
The Apriori Algorithm 35
The FP-Growth Algorithm 43
SPADE 62
DEGSeq 69
K-Means 77
Hybrid Hierarchical Clustering 85
Expectation Maximization (EM) 95
Dissimilarity Matrix Calculation 107
Hierarchical Clustering 113
Density-Based Clustering 120
K-Cores 127
Fuzzy Clustering - Fuzzy C-means 133
RockCluster 142
Biclust 147
Partitioning Around Medoids (PAM) 152
CLUES 164
Self-Organizing Maps (SOM) 167
Proximus 182
CLARA 186
SVM 193
penalizedSVM 203
kNN 213
Outliers 217
Decision Trees 224
Naïve Bayes 232
adaboost 235
JRip 240
RWeka 244
gausspred 245
optimsimplex 246
CCMtools 247
FactoMineR 247
nnet 253
References
Article Sources and Contributors 259
Image Sources, Licenses and Contributors 261
Article Licenses
License 263
Data Mining Algorithms In R
1
Data Mining Algorithms In R
In general terms, Data Mining comprises techniques and algorithms, for determining interesting patterns from large
datasets. There are currently hundreds (or even more) algorithms that perform tasks such as frequent pattern mining,
clustering, and classification, among others. Understanding how these algorithms work and how to use them
effectively is a continuous challenge faced by data mining analysts, researchers, and practitioners, in particular
because the algorithm behavior and patterns it provides may change significantly as a function of its parameters. In
practice, most of the data mining literature is too abstract regarding the actual use of the algorithms and parameter
tuning is usually a frustrating task. On the other hand, there is a large number of implementations available, such as
those in the R project, but their documentation focus mainly on implementation details without providing a good
discussion about parameter-related trade-offs associated with each of them.
This Wikibook aims to fill this gap by integrating three pieces of information for each technique: description and
rationale, implementation details, and use cases. The description and rationale of each technique provide the
necessary background for understanding the implementation and applying it to real scenarios. The implementation
details not only expose the algorithm design, but also explain its parameters, in the light of the rationale provided
previously. Finally, the use cases provide an experience of the algorithms use on synthetic and real datasets.
The choice of the R project as the computational platform associated with this Wikibook stems from its popularity
(and thus critical mass), ease of programming, good performance, and an increasing use in several fields, such as
bioinformatics and finances, among others.
If you want to learn how to program in the R language, read the book R Programming.
Contents
1.1. Dimensionality Reduction
2.2. Frequent Pattern Mining
3.3. Sequence Mining
4.4. Clustering
5.5. Classification
6.6. R Packages
External links
• R Reference Card for Data Mining
[1]
• R Data Mining
[2]
• All basics of R
[3]
• Online course in Data Mining in R
[4]
• Computing for Data Analysis (Free online course)
[5]
Data Mining Algorithms In R
2
References
[1] http:/ / www. rdatamining. com
[2] http:/ / rdatamining. wordpress. com
[3] http:/ / www. cran. r-project. org
[4] http:/ / www. statistics. com/ data-mining-r/
[5] https:/ / class. coursera. org/ compdata-003
Dimensionality Reduction
1.1. Principal Component Analysis
2.2. Singular Value Decomposition
3.3. Feature Selection
Frequent Pattern Mining
Contents
1.1. The Eclat Algorithm
2.2. arulesNBMiner
3.3. The Apriori Algorithm
4.4. The FP-Growth Algorithm
Sequence Mining
1.1. SPADE
2.2. DEGSeq
剩余265页未读,继续阅读
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- wangjianming2342016-12-08非常感谢!很有用的英文学习资料!
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