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Hello, please have a look at the attached file. I need you to implement first stage only: data pre-processing.
Data normalization: continuous features need be converted to discrete. Make sure that all values are in numerical formats.
Use Entropy based feature selection method for selecting the attributes and removing the redundant ones
applies the k-means clustering algorithm to the given dataset to split the data records into normal cluster and anomalous clusters. Specify the number of clusters as five to the k-means and cluster the records in the dataset into normal cluster and anomalous clusters. The anomalous clusters are U2R, R2L, PROBE, and DoS. The records are labeled with the cluster indices. Then, divide the data set into two parts. One part is used for training and the other one is used for evaluation.
Download the below data from: [obscured] /databases/kddcup99/kddcup99.html
kddcup.data_10_percent.gz A 10% subset. (2.1M; 75M Uncompressed)
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