# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
## 4 Experimental Study
### Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Cardio} & \multirow{2}{*}{21} & \multirow{2}{*}{1831} & \multirow{2}{*}{1655} & \multirow{2}{*}{176} & \multicolumn{3}{c}{There are 3 classes, normal, suspect and pathologic.} \\  & & & & & & Pathologic (novelty) class is downsampled to 176 points. \\  & & & & & The suspect class is discarded. \\ \hline Glass & 9 & 214 & 205 & 9 & This dataset contains attributes regarding several glass types (multi-class). Here, class 6 is marked as novelty. \\ \hline Ionosphere & 33 & 351 & 225 & 126 & There is one attribute having values all zeros, which is discarded. So, the total number of dimensions are 33. \\  & & & & & The ’bad’ class is considered as novelty class. \\ \hline Letter & 32 & 1600 & 1500 & 100 & 3 letters from data was sampled to form the normal class and randomly concatenate pairs of them to form the novelty class. \\ \hline Lympho & 18 & 148 & 142 & 6 & It has four classes but two of them are quite small. Therefore, those two small classes are merged and considered as novelty. \\ \hline Mnist & 100 & 7603 & 6903 & 700 & Digit-zero class is considered as inliers, while 700 images are sampled from digit-six class as the outliers. \\ \hline Musk & 72 & 3062 & 2965 & 97 & The non-musk classes, j146, j147 and 252 are combined to form the inliers, while the musk classes 213 and 211 are added as novelty without downsampling. Other classes are discarded. \\ \hline Optdigits & 64 & 5216 & 5066 & 150 & The instances of digits 1-9 are inliers and instances of digit 0 are down-sampled to 150 outliers. \\ \hline Pendigits & 16 & 6870 & 6714 & 156 & It has10 classes (0... 9). In this dataset, all classes have equal frequencies. So, the number of objects in one class (corresponding to the digit “0”) is reduced by a factor of 10\%. \\ \hline Pima & 8 & 768 & 500 & 268 & Several constraints were placed on the selection of instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. \\ \hline Satellite & 36 & 6435 & 4399 & 2036 & The smallest three classes, i.e., 2, 4, 5 are combined to form the outliers’ novelty. \\ \hline Satimage & 36 & 5803 & 5732 & 71 & Class 2 is down-sampled to 71 outliers, while all the other classes are combined to form an inlier class. \\ \hline Suttle & 9 & 49097 & 45586 & 3511 & The smallest 5 classes, i.e., 2, 3, 5, 6, 7 are combined to form the novelty class, while class 1 forms the inlier class. Data for class 4 is discarded. \\ \hline Thyroid & 6 & 3772 & 3679 & 93 & There are 3 classes are built: normal, hyperfunction and subnormal functioning. The hyperfunction class is treated as novelty class. \\ \hline Vertebral & 6 & 240 & 210 & 30 & The following convention is used for the class labels: Normal (NO) and Abnormal (AB). Here, “AB” is the majority class having 210 instances which are used as inliers and “NO” is downsampled from 100 to 30 instances as outliers. \\ \hline WBC & 30 & 378 & 357 & 21 & There are 2 classes, benign and malignant. The malignant class of this dataset is downsampled to 21 points, which are considered as novelty. \\ \hline Wine & 13 & 129 & 119 & 10 & Class 1 is downsampled to 10 instances to be used as novelty. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont\).