DATA MINING REPORTPHASE (1)

LAMIYA EL_SAEDI 220093158

Index

1.1: Introduction 1.2: Descriptions 1.2.1: White wine description 1.2.2: Brest Tissue description 1.3: Conclusion

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.

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

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

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

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

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

Figer:1.2.2.3 the row of outlier data

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

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.