# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
###### Abstract  
Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components to show that the components of the proposed method together are more effective. As a result of the experiment, the DAE-DBC approach is more efficient; its area under the curve (AUC) is shown to be 13.5 percent higher than state-of-the-art algorithms and other versions of the proposed method that we have demonstrated. novelty detection; dimensionality reduction; clustering +
Footnote †: journal: Medical  
Moreover, most of the data are not labeled in the practice, and in this case, an unsupervised method, such as the third, is used. In recent years, research related to unsupervised novelty detection suggest using One-class Support Vector Machine (OC-SVM) based, clustering based, reconstruction error based methods and to combine these methods together. OC-SVM separates normal and novelty data as projecting the input features into the high dimensional feature spaces using a kernel trick. In other words, finding the decision boundary that the farthest isolate the origin and data points and the closest points to the origin considered as the novelty. However, SVM is a memory and time-consuming task in practice and its complexity grows quadratically with the number of records [12]. Cluster analysis divides data into groups that are meaningful, useful, or both [2]. In clustering based novelty detection, there are several ways to identify novelty, such as clusters that are located far away from other clusters, or smaller or sparser than other clusters, or points not belonging to any group are considered as abnormal data. But the cluster-based method performance is very dependent on which algorithm is used as well as high-dimensional data in clustering with distance is special challenges to data mining algorithms [13]. To address this issue caused by the curse of dimensionality, reconstruction error based approach and hybrid approaches are widely used. PCA is a technique used to reduce dimensionality. [PERSON] and [PERSON] introduced diagnosis of anomalies by the first principal component score and its orthogonal distance to each data point [14]. [PERSON] introduced kernel PCA (KPCA), which maps input spaces into higher-dimensional space before performing the PCA using a kernel function for outlier detection [15]. In a hybrid approach, dimensionality reduction first made and then clustering or other classification algorithms are performed in the latent low-dimensional space. In recent publications, unsupervised novelty detection using deep learning is proven by high effectiveness [16; 17; 3; 12; 18]. They use an autoencoder that is an artificial neural network to extract features and to reduce the dimension then give them as an input to the novelty identification methods such as Gaussian mixture model, cumulative distribution function and clustering algorithm and so on. In this paper, the DAE-DBC method is suggested and aims to increase the accuracy of unsupervised novelty detection.