In this paper, we propose a new competitive learning algorithm for training single-layer neural networks to cluster data. The proposed algorithm adopts a new measure based on the idea of "symmetry" so that neurons compete with each other based on the symmetrical distance instead of the Euclidean distance. The detected clusters may be a set of clusters of different geometrical structures. Four data sets are tested to illustrate the effectiveness of our proposed algorithm.

An automatic defect classification system (ADC) for use in visual inspection of semiconductor wafers is introduced. The methods of extracting the defect features based on the human experts' knowledge, with their correlations with the defect classes are elucidated. As for the classifier, Hyperellipsoid Clustering Network (HCN) which is a layered network model employing second order discrimination borders in the feature space, is introduced. In the experiments using a collection of defect images, the HCNs are compared with the conventional multilayer perceptron networks. There, it is shown that the HCN's adaptive hyperellipsoidal discrimination borders are more suited for the problem. Also, the cluster encapsulation by the hyperellipsoidal border enables to determine rejection classes, which is also desirable when the system will be in actual use. The HCN with rejection achieves, an overall classification rate of 75% with an error rate of 18%, which can be considered equivalent to those of the human experts.

This paper addresses the optimum routing problem of multipoint connection in large-scale networks. A number of algorithms for routing of multipoint connection have been studied so far, most of them, however, assume the availability of complete network information. Herein, we study the problem under the condition that only partial information is available to routing nodes and that routing decision is carried out in a distributed cooperative manner. We consider the network being partitioned into clusters and propose a cluster-based routing approach for multipoint connection. Some basic principles for network clustering are discussed first. Next, the original multipoint routing problem is defined and is divided into two types of subproblems. The global optimum multicast tree then can be obtained asymptotically by solving the subproblems one after another iteratively. We propose an algorithm and evaluate it with computer simulations. By measuring the running time of the algorithm and the optimality of resultant multicast tree, we show analysis on the convergent property with varying network cluster sizes, multicast group sizes and network sizes. The presented approach has two main characteristics, 1) it can yield asymptotical optimum solutions for the routing of multipoint connection, and 2) the routing decisions can be made in the environment where only partial information is available to routing nodes.

This paper presents a new multi-clustering network for the purpose of intelligent data classification. In this network, the first layer is a self-organized clustering layer and the second layer is a restricted clustering layer with a neighborhood mechanism. A new clustering algorithm is developed in this system for the efficiently use of parallel processors. This parallel algorithm enables the nodes of this network to be independently processed in order to minimize data communication load among processors. Using the parallel processors, the quite low calculation cost can be realized among the conventional networks. For example, a 4-processor parallel computing system has shown its ability to reduce the time taken for data classification to 26.75% of a single processor system without declining its performance.

In this paper we discuss a certain constrained optimization problem which is often encountered in the geometrical optimization. Since these kinds of problems occur frequently, constrained genetic optimization becomes very important topic for research. This paper proposes a new methodology to handle constraints using the Genetic Algorithm through a multiprocessor system (FIN) which has a self-similarity network.

A mathematical model based on an optimization formulation is presented for perceptual clustering of dot patterns. The features in the present model are its nonlinearity enabling the model to reveal hysteresis phenomena and its scale invariance. The clustering of dots is given by the mutual linking of dots by virtual lines. Every dot is assumed to be perceived at locations displaced from their original places. It is exemplified with simulations that the model can produce a hierarchical clustering of dots by variation in thresholds for the wiring of virtual lines and also the model can additionally reproduce some geometrical illusions semiquantitatively. This model is further extended for perceptual grouping in line segment patterns and geometrical illusions obsrved in those patterns are reproduced by the extended model.

A method based on clustering is presented for restoring and segmenting gray scale images. An optimum clustering obtained by a gradient method gives an image with gray scale values which vary smoothly in each segmented region. The method is also applied to restoration from sparsely sampled data.

A new approach of data clustering which is capable of detecting linked or crossed clusters, is proposed. In conventional clustering approaches, it is a hard work to separate linked or crossed clusters if the cluster prototypes are difficult to be represented by a mathematical formula. In this paper, we extract the force information from data points using the concept of psychological potential field, and utilize the information to measure the similarity between data points. Through several experiments, the force shows its effectiveness in diiscriminating different clusters even if they are linked or corssed.

This paper proposes a multi-layer cellular network in which a self-organizing method is implemented. The network is developed for the purpose of data clustering and recognition. A multi-layer structure is presented to realize the sophisticated combination of several sub-spaces which are spanned by given input characteristic data. A self-organizing method is useful for evaluating the set of clusters for input data without a supervisor. Thus, using these techniques this network can provide good clustering ability as an example for image/pattern data which have complicated and structured characteristics. In addition to the development of this algorithm, this paper also presents a parallel VLSI architecture for realizing the mechanism with high efficiency. Since the locality can be kept among all processing elements on every layer, the system is easily designed without large global data communication.

An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.