## 1 Introduction  
Shearer is one of the key pieces of equipment in a fully-mechanized coal mining workspace. It is of great significance to guarantee long-term and steady operation of the shearer for avoiding safety incidents and improving the output of coal mining. Many scholars pointed that recognizing whether the shearer was cutting coal seams, hard rock or coal seam gripping gangue, defined as cutting pattern recognition in this paper, was the precondition for secure and efficient coal mining [1]. However, because of the harsh working environment and complicated geological condition during the coal mining process, online recognition was always a technological bottleneck, and the identification rate was directly affected by the signal source during the recognition process. Since the cutting pattern recognition was proposed in the 1970s, great academic achievements have scored. More than 20 types of signals were researched in the past several decades, such as \(\gamma\)-rayconcentration [2], cutting image [3], cutting temperature [4], vibration of the shearer [5], etc. All of these methods promoted the theory and experimental developments of the cutting pattern recognition, but also encountered some common problems during practical application. In [3], texture features of cutting images in multiple scales were collected to identify the cutting pattern automatically. Similar to the \(\gamma\)-ray detector, the high-resolution image receiver had the disadvantages of huge size and power consumption. Some special treatments were needed to satisfy the safety standards of the coal mining explosion-proof electric apparatus. Recently, [PERSON] et al. fused the temperature information of the shearer through an improved D-S evidence theory and artificial neural network to predict the coal rock distribution [4]. In order to recognize the coal-rock interface in the top coal caving, the vibration signals of the tail beam of the hydraulic support were investigated in [5], while the temperature and vibration signals could be collected only by contact measurement, which was not convenient for installation and maintenance. Manual guidance was necessary and unmanned coal mining was still unachievable in mining production. Through the embedded analysis and thoughts on the working field, it was found that the cutting acoustic signal, formed by the collision of the cutting part of the shearer and the coal-rock, had a relation with the cutting pattern [6]. Moreover, the sound collector had obvious superiorities of small size, low-power and non-contact measurement. The acoustic-based cutting pattern recognition system is actually excavating effective information and then classifying the real-time signal series into some patterns. It is essentially a process of clustering. Clustering involves the discovery of a data structure in multivariate information and divides a data set into several subsets without providing prior knowledge [7; 8]. The clustering process is often regarded as the important first step for data exploration. The most widely applied clustering methods can be divided into hard partitioning algorithms and soft partitioning algorithms [9; 10]. Typical hard partitioning solutions, such as K-means [11; 12; 13] and K-medoids clustering [14], assign each pattern into a cluster according to its nearest centroid. Then, positions of the centroids are iteratively adjusted according to their corresponding members in order to minimize the sum of the squared error. Fuzzy C-means (FCM) algorithm is a kind of representative soft partitioning approach, which allows each individual to belong into all clustering centers according to the degree of membership [15]. In practical application, many scholars point out that FCM often performs better than the hard partitioning algorithm. In addition, it is widely applied in fault diagnosis [16], state recognition [17], image segmentation [18], et al.