# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
## 5 Conclusions  
By this paper, we have proposed DAE-DBC approach to detect novelty on unsupervised mode by combining deep autoencoder for low dimensional representation and clustering techniques for novelty estimation. This approach can classify unlabeled dataset with different ratio with normal and novelty by calculating optimal parameters for error threshold and clustering algorithm. We have demonstrated that the components in suggested approach in the experiment has given more efficient results together. DAE-DBC algorithm has improved average AUC of 9 state-of-the-art algorithms on 20 public benchmark datasets by 13.5 percent. Therefore, we suggest this approach on high dimensional data to make unsupervised novelty detection.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON] supervised, checked, gave comments and approved this work.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**RapidMiner/Extension of Outlier Detection/**} & \multicolumn{3}{c}{**Python Implementation/Using Keras/**} \\ \hline \hline Annthyroid & 40 & 66 & 46 & 46 & 0.11 & 0.25 & 24.662 & 0.467 & 28.491 & 26.576 & 33.581 & 2002.1 & 261.46 \\ Breastw & 44 & 7 & 46 & 6 & 0.153 & 0.463 & 0.477 & 0.492 & 0.666 & 22.22 & 21.874 & 115.32 & 31.764 \\ Arrhythmia & 0\(<\) & NA & 1 & 0\(<\) & 0.063 & 0.082 & 0.206 & 0.111 & 0.394 & 6.737 & 9.155 & 105.33 & 32.916 \\ Cardio & 12 & 24 & 15 & 16 & 0.107 & 0.227 & 1.564 & 0.326 & 1.824 & 9.027 & 7.735 & 152.27 & 71.649 \\ Glass & 0\(<\) & 0\(<\) & 0\(<\) & 0\(<\) & 0.025 & 0.034 & 0.059 & 0.084 & 0.156 & 8.164 & 5.286 & 32.869 & 14.515 \\ Ionosphere & 0\(<\) & 0\(<\) & 0\(<\) & 0\(<\) & 0.041 & 0.069 & 0.115 & 0.138 & 0.263 & 32.845 & 5.919 & 39.85 & 19.17 \\ Letter & 5 & 11 & 7 & 7 & 0.072 & 0.267 & 1.349 & 0.315 & 1.574 & 9.577 & 9.75 & 113.1 & 60.347 \\ Lymphho & 0\(<\) & 0\(<\) & 0\(<\) & 0\(<\) & 0\(<\) & 0.027 & 0.041 & 0.164 & 0.077 & 0.219 & 3.613 & 7.435 & 22.957 & 15.056 \\ Mnist & 1330 & NA & 1134 & 1111 & NA & 3.378 & 41.538 & 2.789 & 43.302 & 27.391 & 35.814 & 754.57 & 268.66 \\ Musk & 50 & 90 & 71 & 73 & 0.204 & 0.691 & 6.744 & 0.660 & 7.116 & 10.253 & 10.989 & 322.09 & 136.11 \\ Ordigits & 124 & 201 & 142 & 146 & NA & 0.923 & 15.405 & 0.997 & 1.685 & 26.592 & 79.213 & 358.509 & 59.33 & 180.97 \\ Pendigits & 31 & 42 & 31 & 36 & 0.262 & 0.561 & 23.313 & 0.656 & 24.260 & 23.485 & 30.98 & 637.16 & 221.27 \\ Pima & 0\(<\) & 0\(<\) & 0\(<\) & 0\(<\) & 0.037 & 0.112 & 0.360 & 0.166 & 0.497 & 7.867 & 5.266 & 80.555 & 28.045 \\ Satellite & 66 & 96 & 71 & 78 & 0.207 & 0.685 & 21.680 & 0.798 & 24.556 & 22.7289 & 88.587 & 686.855 & 236.33 \\ Satimage & 42 & 58 & 44 & 55 & 0.187 & 0.642 & 17.275 & 0.849 & 18.386 & 24.192 & 21.31 & 592.40 & 217.29 \\ S