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Data Mining Report Phase: Exploring White Wine and Brest Tissue Datasets

This report covers the preprocessing phase of data mining, focusing on two datasets: White Wine (CSV) and Brest Tissue (XLS). Utilizing the Rabid Miner program, we apply various techniques including data cleaning, outlier detection, and attribute removal. The White Wine dataset is discretized to classify quality, while the Brest Tissue dataset undergoes outlier processing. The findings highlight the importance of thorough preprocessing to ensure data accuracy and suitability for subsequent analysis phases.

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Data Mining Report Phase: Exploring White Wine and Brest Tissue Datasets

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  1. DATA MINING REPORTPHASE (1)LamiyaEl_Saedi 220093158

  2. Index • 1.1: Introduction • 1.2: Descriptions • 1.2.1: White wine description • 1.2.2: Brest Tissue description • 1.3: Conclusion

  3. 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.

  4. 1.2 DESCRIPTIONS1.2.1: 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 3 Good from 4 to 5 Very good from 6 to 7 Excellent from 8 to 10

  5. Discretize process Figure 1.2.1.1: the model of discretize process

  6. continue • Figure 1.2.1.2: the output of discretize method

  7. Sample process and Remove correlate attribute • Figure 1.2.1.3: Sample process and Remove correlate attribute on white wine dataset

  8. continue • Figure 1.2.1.5: result of sample process and remove correlation attribute on white wine dataset

  9. filter process • Figure 1.2.1.6 filter example process on white win dataset

  10. continue 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

  11. 1.2.2: Brest tissue descriptiondetect outlier • Figure 1.2.2.1: outlier process on Brest tissue dataset

  12. continue • Figure: 1.2.2.2 plot outlier method on Brest tissue dataset

  13. Figer:1.2.2.3 the row of outlier data

  14. 2- Remove correlated attribute : Figure 1.2.2.4: remove correlated attribute from Brest tissue dataset

  15. continue Figure 1.2.2.5: the remain attribute after execute the remove correlation process from Brest tissue

  16. 1.3: CONCLOSION • 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.

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