 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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Presentation transcript:

 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.

 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 : the model of discretize process

 Figure : the output of discretize method

 Figure : Sample process and Remove correlate attribute on white wine dataset

 Figure : result of sample process and remove correlation attribute on white wine dataset

 Figure filter example process on white win dataset

Figure : sweet white wine based on Syria measurements Figure : non sweet white win based on Syria measurements

 Figure : outlier process on Brest tissue dataset

 Figure: plot outlier method on Brest tissue dataset

Figer: the row of outlier data

Figure : remove correlated attribute from Brest tissue dataset

Figure : 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.