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DATA MINING REPORTPHASE (1)
LAMIYA EL_SAEDI 220093158
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Index
1.1: Introduction 1.2: Descriptions 1.2.1: White wine description 1.2.2: Brest Tissue description 1.3: Conclusion
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1.1 :INTRODUCTION
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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1.2 DESCRIPTIONS 1.2.1: white wine description
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 3Good from 4 to 5Very good from 6 to 7Excellent from 8 to 10
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Discretize process
Figure 1.2.1.1: the model of discretize process
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Figure 1.2.1.2: the output of discretize method
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Sample process and Remove correlate attribute
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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filter process
Figure 1.2.1.6 filter example process on white win dataset
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Figure 1.2.1.8: sweet white wine based on Syria measurements
Figure 1.2.1.7: non sweet white win based on Syria measurements
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1.2.2 :Brest tissue descriptiondetect outlier
Figure 1.2.2.1: outlier process on Brest tissue dataset
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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
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2 -Remove correlated attribute :
Figure 1.2.2.4: remove correlated attribute from Brest tissue dataset
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Figure 1.2.2.5: the remain attribute after execute the remove correlation process from Brest tissue
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1.3 :CONCLOSION1. 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.