# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 1 Introduction  
Time-series clustering [1, 2], just like time-series classification [3] and time-series prediction [4, 5], is one of the data mining methods of time-series. Time-series clustering is used to extract useful information from the data curve and divide the unmarked data into different clusters for maximizing the similarity of objects in the same cluster and the divergence of objects between different clusters [6]. It has been widely applied in many fields, for example, finance, biomedicine, environment, and so forth. Many kinds of studies have been conducted on time-series clustering. For example, [PERSON] [7] proposed a new K-Means-type smooth subspace clustering algorithm for clustering time-series data. [PERSON] [8] demonstrated that a least-squares polynomial segmentation procedure could be applied to each time-series to return different-length segments. Then, all the segments were projected into the same dimensional space, according to the coefficients of the model. [PERSON] [9] proposed a fuzzy time-series forecasting model based on multiple linear regression and time-series clustering for forecasting market prices. Besides the clustering algorithm itself, distance measurement is another key factor affecting the clustering performance of time series. Many different distance measurement methods are adopted and compared for time-series data, including Hausdorff distance [10], Minkowski [11], hidden Markov model-based distance [12], Euclidean distance [13], and dynamic time warping (DTW) [14]; the last two are the most widely used [15]. Although many clustering algorithms and distance measurement methods have been applied together, these traditional clustering methods often have poor classification performance and usually do not perform well on high-dimensional data. However, image clustering, according to the value of image pixels, is a key technique for better accomplishing image annotation and searching in large image repositories. The aforementioned clustering can quickly extract obvious features from images and achieve clustering. Due to the rapid development of deep learning and its inherent characteristics, the deep neural network is used to transform data into good clustering representation. The existing deep clustering models for images can be divided into three categories according to the network structure: based on automatic encoder (AE) methods, based on CDNN methods, and based on generation model methods. The former can be regarded as encoders and decoders, which are used for feature mapping and reconstruction, respectively, including deep clustering networks [16], deep embedding networks [17], deep subspace clustering networks [18], and deep manifold clustering [19]. The methods based on CDNN have three types of network architectures, including deep belief networks [20], fully convolutional networks, and convolutional neural networks (CNN). Deep nonparametric clustering [21], deep embedded clustering [22], and discriminatively boosted image clustering [23] are the classical unsupervised preprocessing network models among these kinds of methods. Besides the latter, based on variational autoencoder and generative adversarial networks [24], such as variational deep embedding [25] and deep adaptive image clustering [26], are proposed for clustering and sample generation. Therein, the clustering method based on CDNN can be used to extract more distinctive features and can cluster large-scale image datasets. The depth clustering algorithms mentioned above are often based on two-dimensional images. At present, most time series clustering algorithms are based on traditional methods and show low accuracy. Therefore, a model based on CDNN is proposed to convert time series into images and improve the clustering performance by the depth learning methods.