A hybrid clustering algorithm based on improved GWO and KHM clustering

To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.

An Efficient Hybrid Clustering and Feature Extraction Techniques for Brain Tumor Classification

Most aggressive and common disease is Brain tumors and it leads to very short life expectancy in its highest grade. For proper treatment, such tumors needs to be identified in early stages and detecting brain tumors, medical imaging is used as an important tool. Although, for diagnosing such tumors, MRI (Magnetic Resonance Imaging) is used very often and it is assumed as a highly suitable technique. From brain magnetic resonance imaging (MRI) data, edema and tumor inference is a challenging task due to brain tumors blurred boundaries, complex structure and external factors like noise. For alleviating noise sensitivity and enhancing segmentation stability, a hybrid clustering algorithm is proposed in this research work. Certain processes like classification, feature extraction, hybrid clustering and pre-processing are included in this proposed model. For segmentation of brain tumors, proposed a morphological operation. Skull stripping and contrast enhancement are two process performed in pre-processing stage. It is possible to detect high contrast regions under contrast enhancement. In second stage, Enhanced K- means algorithm is combined with Fuzzy C- Means Clustering (FCM), where images are segmented as clusters. Algorithm’s stability can be enhanced using this clustering techniques while minimizing clustering parameter’s sensitivity. Segmented objects are converted into representations using representation and feature extraction techniques. Major attributes and features are described in a better manner using these techniques. The Fast Discrete Curvelet Transform (FDCT) is used for performing feature extraction in this technique for minimizing complexity and enhancing performance. At last, for classification, deep belief network (DBN) is used in this work. And it uses the concept of optimized DBN, for which Improved dragonfly optimisation algorithm (IDOA) is utilized. This proposed model is termed as IDOA-DBN model. When compared with other classification techniques, brain tumors can be detected effectively using proposed model.

Wafer Pattern Recognition for Detecting Process Abnormalities in NAND Flash Memory Manufacturing

We have adopted various defect detection systems in the front stage of manufacturing in order to effectively manage the quality of flash memory products. In this paper, we propose an intelligent pattern recognition methodology which enables us to discriminate abnormal wafer automatically in the course of NAND flash memory manufacturing. Our proposed technique consists of the two steps: pre-processing and hybrid clustering. The pre-processing step based on process primitives efficiently eliminates noisy data. Then, the hybrid clustering step dramatically reduces the total amount of computing, which makes our technique practical for the mass production of NAND flash memory.