Extending the Kohonen self-organizing map networks for clustering analysis

A fuzzy self-organizing map (SOM) network is proposed in this paper for visual motor control of a 7 degrees of freedom (DOF) robot manipulator. The inverse kinematic map from the image plane to joint angle space of a redundant manipulator is highly nonlinear and ill-posed in the sense that a typical end-effector position is associated with several joint angle vectors. In the proposed approach, the robot workspace in image plane is discretized into a number of fuzzy regions whose center locations and fuzzy membership values are determined using a Fuzzy C-Mean (FCM) clustering algorithm. SOM network then learns the inverse kinematics by on-line by associating a local linear map for each cluster. A novel learning algorithm has been proposed to make the robot manipulator to reach a target position. Any arbitrary level of accuracy can be achieved with a number of fine movements of the manipulator tip. These fine movements depend on the error between the target position and the current manipulator position. In particular, the fuzzy model is found to be better as compared to Kohonen self-organizing map (KSOM) based learning scheme proposed for visual motor control. Like existing KSOM learning schemes, the proposed scheme leads to a unique inverse kinematic solution even for a redundant manipulator. The proposed algorithms have been successfully implemented in real-time on a 7 DOF PowerCube robot manipulator, and results are found to concur with the theoretical findings.

This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem of the poor performance of a single clusterer. Cascaded SOM is an extension of classical SOM combined with the cascaded structure. The method combines the outputs of multiple SOM networks in a cascaded manner using them as an input to another SOM network. It also utilizes the characteristic of high-dimensional data insensitivity to changes in the values of a small number of dimensions to achieve the effect of ignoring part of the SOM network error output. Since the initial parameters of the SOM network and the sample training order are randomly generated, the model does not need to provide different training samples for each SOM network to generate a differentiated SOM clusterer. After testing on several classical datasets, the experimental results show that the model can effectively improve the accuracy of pattern recognition by 4%∼10%.

The objective of this work is to develop a digitized mammogram feature extraction approach using Kohonen's self-organizing maps (SOM). Once developed, the SOM network can be used as the first processing stage in a breast cancer computer aided diagnosis system. Its role is to offer segmented data as input to a second stage dedicated to the diagnosis task, which is implemented via a multilayer perceptron trained by the backpropagation algorithm.

Binary tree time adaptive self-organizing map

Independent component analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, a novel compound neural network for fault sources recognition was proposed. First, neural ICA algorithm was applied to fusion of multi-channel measurements by sensors. Moreover, further feature extraction was made. Thus, statistical features higher than second order were captured from the measurements. Second, a typical neural classifier such as the back-propagation (BP), the radial basis function (RBF) or the SOM network was trained for the final fault sources recognition. The results from contrast experiments in fault diagnosis of rotating machines show that the proposed compound neural network with ICA based feature extraction can recognize various fault sources at considerable accuracy, and be constructed in simpler way, both of which imply its great potential in fault diagnosis.

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

A modular neural network architecture has been used for the classification of remote sensed data in two experiments carried out to study two different but rather usual situations in real remote sensing applications. Such situations concern the availability of high-dimensional data in the first setting and an imperfect data set with a limited number of features in the second. The learning task of the supervised multilayer perceptron classifier has been made more efficient by preprocessing the input with unsupervised neural modules for feature discovery. The linear propagation network is introduced in the first experiment to evaluate the effectiveness of the neural data compression stage before classification, whereas in the second experiment data clustering before labeling is evaluated by the Kohonen self-organizing feature map network. The results of the two experiments confirm that modular learning performs better than nonmodular learning with respect to both learning quality and speed. © 1996 Society of Photo-Optical Instrumentation Engineers. Subject terms: neural network; remote sensing; classification; supervised and un- supervised learning.

A neural piecewise linear classifier, based on the Kohonen learning vector quantization (LVQ2) and the Kohonen self-organizing feature map is proposed. The classifier has two stages and a feedback loop. In the first stage, the Kohonen self-organizing feature map network is used to find the approximate position of the prototype vectors for each class. In the second stage, the Kohonen LVQ2 supervised learning algorithm is used to fine-tune the position of the approximate prototype vectors. The accuracy of the classifier is improved by adding an adaptive feedback scheme. Depending on the intrinsic complexity of the class distribution and overall partitioning of the space, the neural classifier automatically increases the number of neurons, improving the error performance. The classifier was tested on a set of high-dimensional real data obtained from ship images. The performance is compared with a piecewise linear tree classifier and a neural classifier. >

Self-Organizing Map Based Approach for Assessment of Control Room Operator Training

The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight, and the cluster center is the “food” of the particle group. Each particle moves toward the nearest cluster center. Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration. After a lot of experimental analysis on the commonly used UCI data set, this paper not only solves the shortcomings of K-means clustering algorithm, the problem of dependence of the initial clustering center, and improves the accuracy of clustering, but also avoids falling into the local optimum. The algorithm has good global convergence.

This paper proposes an efficient purchase pattern clustering method based on SOM(Self-Organizing Map) for Personal Ontology Recommender System in u-Commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to keep clustering the user’s information to join the user’s score based on RFM factors using SOM network and the analysis of RFM to be able to reflect the attributes of the user in order to reflect frequently changing trends of purchase pattern by emphasizing the important users and items, and to improve better performance of recommendation. The proposed makes the task of an efficient purchase pattern clustering based on SOM for preprocessing so as to be possible to recommend by the loyalty of RFM factors as considering user’s propensity. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

For the low-speed marine diesel engine economic performance analysis, a condition monitoring method with engine degradation contribution calculation is mainly considered in this paper. The diesel engine economic performance baseline is firstly modeled with the historical, fault-free monitoring data, including the fuel economy evaluation baseline with nonlinear function fitting and the engine component healthy baseline with the Self-Organizing Maps (SOM) network. Then, with the Self-Organizing Map - Minimum Quantization Error (SOM-MQE) method for the degradation contribution calculation, the correlative component anomaly can be diagnosed while the economic performance deviated from the baseline. Based on the performance analysis, the maintenance suggestion support can be provided as well. Results of the presented method are verified by the studies of the real low-speed diesel engine monitoring case on a Panamax bulk carrier.

The purpose of this article is to investigate customer clustering based on big data of the consumer loyalty system. The object of the research is the retail chains that are the clients of the Maxbonus system. Self-organizing Kohonen maps, implemented by the selforgmap function of the Deep Learning Toolbox module of the Matlab system, were used as a clustering tool. As a result of clustering 990 customers, classes invariant to the partition were identified, and it was shown that their number stabilizes with an increase in the number of classes in the Kohonen self-organizing map. The average indicators of representatives of each class have been determined. This result indicates the efficiency of the approach for clustering customers of customer loyalty systems. The prospect of the work is to include in the number of input parameters clustering the volume of purchases by product categories. Also promising is the transition to clustering retail chains participating in the Maxbonus system.

This paper represents TSP (Travelling Salesman Problem) by using Artificial Neural Networks.A comparative study of various methods of ANN is shown here for solving TSP problem.The Travelling Salesman Problem is a classical combinational optimization problem, which is a simple to state but very difficult to solve. This problem is to find the shortest possible tour through a set of N vertices so that each vertex is visited exactly once. TSP can be solved by Hopfield Network, Self-organization Map, and Simultaneous Recurrent Network. Hopfield net is a fully connected network, where every vertex is connected with each other forwardly and backwardly. So starting the walk from a vertex we can travel all the other vertex exactly once and return to starting vertex again.

Utilization of remote sensing multi- and hyperspectral imagery has shown a rapid increase in many areas of economic and scientific significance over the past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, however, has proved an insurmountable challenge for many traditional classification, clustering and visualization methods. This chapter presents spectral image analyses with Self-Organizing Maps (SOMs). Several recent extensions to the original Kohonen SOM are discussed, emphasizing the necessity of faithful topological mapping for correct interpretation. The effectiveness of the presented approaches is demonstrated through case studies on real-life multi- and hyperspectral images.