Transcript DATA MINING REPORT PHASE (1) Lamiya El_Saedi 220093158

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1.1: Introduction
1.2: Descriptions
1.2.1: White wine description
1.2.2: Brest Tissue description
1.3: Conclusion
In this phase we discuss the first step in data mining
PREPROCESSING on two datasets.
The first one is an CSV file talked about White Wine, and the other is
an XLS file talked about Brest Tissue.
We work on Rabid Miner program.
In this phase we will use plot data to understanding, find the outlier in
data cleaning.
Remove attribute (columns) which are not related to each other,
set roles to convert target class from regular to label in data
transformation.
And using sampling from large data in data reduction.
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Methods:
1- Discretize process:
In this method we choose quality as target class
which is take values from 0 to 10 to represent
quality of white wine from bad to excellent as a
new classification.
We added four classes :
Bad from –infinity to 3
Good from 4 to 5
Very good from 6 to 7
Excellent from 8 to 10
Figure 1.2.1.1: the model of discretize process
Figure 1.2.1.2: the output of discretize method
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Figure 1.2.1.3: Sample process and Remove
correlate attribute on white wine dataset
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Figure 1.2.1.5: result of sample process and
remove correlation attribute on white wine
dataset
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Figure 1.2.1.6 filter example process on white
win dataset
Figure 1.2.1.7: non sweet white win based on
Syria measurements
Figure 1.2.1.8: sweet white wine based on Syria
measurements
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Figure 1.2.2.1: outlier process on Brest tissue
dataset
Figure: 1.2.2.2 plot outlier method on Brest tissue
dataset
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Figer:1.2.2.3 the row of outlier data
Figure 1.2.2.4: remove correlated attribute from
Brest tissue dataset
Figure 1.2.2.5: the remain attribute after execute the
remove correlation process from Brest tissue
1. Preprocessing phase is very important to prepare your
data for next phases, and be comfortable your data are
correct.
2. You must input your data set as it is extension type
3. When input the attribute you must choose correct data
type to work on it with more flexibility.
4. Methods maybe not satisfy for other data set, because
each data set has specific characteristics.
5. if you have a sample process in a model every time you
can get a deferent results.