Since we can accumulate a large amount of data including useless information in recent years, it is important to investigate various extraction method of clusters from data including much noises. The Self-Organizing Map (SOM) has attracted attention for clustering nowadays. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction. TSOM consists of two kinds of SOM whose features are different, namely, one self-organizes the area where input data are concentrated, and the other self-organizes the whole of the input space. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. We apply TSOM to data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that we do not know the number of clusters in advance.

We propose in this paper a SOM-like algorithm that accepts online, as inputs, starts and ends of viewing of a multimedia content by many users; a one-dimensional map is then self-organized, providing an approximation of density distribution showing how many users see a part of a multimedia content. In this way "viewing behavior of crowds" information is accumulated as experience accumulates, summarized into one SOM-like network as knowledge is extracted, and is presented to new users as the knowledge is transmitted. Accumulation of multimedia contents on the Internet increases the need for time-efficient viewing of the contents and the possibility of compiling information on many users' viewing experiences. In the circumstances, a system has been proposed that presents, in the Internet environment, a kind of summary of viewing records of many viewers of a multimedia content. The summary is expected to show that some part is seen by many users but some part is rarely seen. The function is similar to websites utilizing "wisdom of crowds" and is facilitated by our proposed algorithm.

Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are connected and we obtain a tour. Basic experimental results suggest that we can find semi-optimal solution much faster than serial methods.

Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.

In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOM for nonuniform input data is investigated. Furthermore, we show its application to clustering and confirm its efficiency by comparing with the k-means method.

In this paper, we propose a modified reproduction strategy of a Genetic Algorithm (GA) utilizing a Self-Organizing Map (SOM) with a novel updating rule of binary weight vectors based on a significance of elements of inputs. In this rule, an updating order of elements is decided by considering fitness values of individuals in a population. The SOM with the proposed updating rule can realize an effective reproduction.

The location information of ubiquitous objects is one of the key issues for context-aware systems. Therefore, several positioning systems to obtain precise location information have been researched. However, they have scalability and flexibility problems because they need completely configured space with a large number of sensors. To avoid the problems, we proposed a self-organizing location estimation method that uses ad hoc networks and Self-Organizing Maps and needs no prepared space with a large number of sensors. But, as in other similar precise localization methods, the proposed method needs advanced distance measurements unavailable to conventional wireless communication systems. In this paper, the self-organizing location estimation method's modification for distance measurement that uses received signal strength available to conventional wireless communication systems but which fluctuates uncertainly, is described and location estimation accuracy with the modified method is shown.

The hand posture estimation system by searching a similar image from a vast database, such as our previous research, may cause the increase of processing time, and prevent realtime controlling of a robot. In this study, the authors proposed a new estimation method of human hand posture by rearranging a large-scale database with the Self-Organizing Map including self-reproduction and self-annihilation, which enables two-step searches of similar image with short period of processing time, within small errors, and without deviation of search time. The experimental results showed that our system exhibited good performance with high accuracy within processing time above 50 fps for each image input with a 2.8 GHz CPU PC.

This paper describes a two-dimensional clustering scheme-based analysis of audible noises induced at telephone terminals. To analyze EMI sources that cause telephone-audible noise, we use a self-organizing map, which provides a way to map high-dimensional data onto a two-dimensional domain. Also, in order to discriminate EMI sources without using particular resonance frequencies that have peaks in the frequency domain, we use the energy spectra of telephone-audible noises as input for training the self-organizing map. In applying this method in actual environments, we measured ten kinds of telephone-audible noises (due to Radio waves and cross-talk noises, etc.) and then derived their energy spectra for eight frequency bands: 1-250 Hz, 250-500 Hz, 500-1 kHz, 1 k-1.5 kHz, 1.5 k-2 kHz, 2 k-3 kHz, 3 k-4 kHz, and over 4 kHz. We visually confirmed that the measured telephone-audible noise data could be projected onto the map in accordance with their properties, resulting in a combined depiction of the composition of derived energy spectra in the frequency bands. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the projected data positions give us an effective depiction of EMI sources that cause disturbances at telephone terminals.

A self-organizing wireless network has to deal with reliability and congestion problems when the network size increases. In order to alleviate such problems, we designed and analyzed protocols and algorithms for a reliable and efficient multiple-layer self-organizing wireless network architecture. Each layer uses a high-power root node to supervise the self-organizing functions, to capture and maintain the physical topology, and to serve as the root of the hierarchical routing topology of the layer. We consider the problem of adding a new root with its own rooted spanning tree to the network. Based on minimum-depth and minimum-load metrics, we present efficient algorithms that achieve optimum selection of root(s). We then exploit layer scheduling algorithms that adapt to network load fluctuations in order to optimize the performance. For optimality we consider a load balancing objective and a minimum delay objective respectively. The former attempts to optimize the overall network performance while the latter strives to optimize the per-message performance. Four algorithms are presented and simulations were used to evaluate and compare their performance. We show that the presented algorithms have superior performance in terms of data throughput and/or message delay, compared to a heuristic approach that does not account for network load fluctuations.

We propose an adaptive plastic-landmine visualizing radar system employing a complex-valued self-organizing map (CSOM) dealing with a feature vector that focuses on variance of spatial- and frequency-domain inner products (V-CSOM) in combination with aperture synthesis. The dimension of the new feature vector is greatly reduced in comparison with that of our previous texture feature-vector CSOM (T-CSOM). In experiments, we first examine the effect of aperture synthesis on the complex-amplitude texture in space and frequency domains. We also compare the calculation cost and the visualization performance of V- and T-CSOMs. Then we discuss merits and drawbacks of the two types of CSOMs with/without the aperture synthesis in the adaptive plastic-landmine visualization task. The V-CSOM with aperture synthesis is found promising to realize a useful plastic-landmine detection system.

A block-matching-based self-organizing map (BMSOM) is presented. Finding a winner is carried out for each block, which is a set of neurons arranged in square. The proposed learning process updates the reference vectors of all of the neurons in a winner block. Then, the degrees of vector modifications are mainly controlled by the size (i.e., the number of neurons) of the winner block. To prevent a single cluster with neurons from splitting into some disjointed clusters, the restriction of the block size is imposed in the beginning of learning. At the main stage, this restriction is canceled. In BMSOM learning, the size of a winner block does not always decrease monotonically. The formula used to update the reference vectors is basically uncontrolled by time. Therefore, even if a map is in a nonstationary environment, training the map is probably pursued without interruption to adjust time-controlled parameters such as learning rate. Numerical results demonstrate that the BMSOM makes it possible to improve the plasticity of maps in a nonstationary environment and incremental learning.

For a kernel-based topographic map formation, kMER (kernel-based maximum entropy learning rule) was proposed by Van Hulle, and some effective learning rules related to kMER have been proposed so far with many applications. However, no discusions have been made concerning the determination of the number of units in kMER. This letter describes a unit-pruning rule, which permits automatic contruction of an appropriate-sized map to acquire the global topographic features underlying the input data. The effectiveness and the validity of the present rule have been confirmed by some preliminary computer simulations.

This paper describes a new fast learning algorithm for Self-Organizing Map employing a "rough comparison winner-take-all" and its digital hardware architecture. In rough comparison winner-take-all algorithm, the winner unit is roughly and strictly assigned in early and later learning stage, respectively. It realizes both of high accuracy and fast learning. The digital hardware of the self-organizing map with proposed WTA algorithm is implemented using FPGA. Experimental results show that the designed hardware is superior to other hardware with respect to calculation speed.

Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the constructive and the pruning procedures, the proposed algorithm finds a near-optimal network which is compact and shows good generalization performance. One of its important features is reliability, which means the randomness of neural networks is effectively reduced. The resultant networks can have suitable numbers of hidden neurons and hidden layers according to the complexity of the given task. The simulation results for the well-known function regression problems show that our method successfully organizes near-optimal networks.

This paper presents a simple learning algorithm for network formation. The algorithm is based on self-organizing maps with growing cell structures and can adapt input data which correspond to nodes of the network. In basic numerical experiments, as a parameter is selected suitably, our algorithm can generate network having small-world-like structure. Such network structure appears in some natural networks and has advantages in practical systems.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

This paper describes an analysis of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, this paper used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. Also, in the LRD-based analysis, this paper employed detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. In applying this method to traffic analysis, this paper performed two kinds of traffic measurement: one based on IP-network traffic flowing into NTT Musashino R&D center (Tokyo, Japan) from the Internet and the other based on IP-network traffic flowing through at an interface point between an access provider (Tokyo, Japan) and the Internet. Based on sequential measurements of IP-network traffic, this paper derived corresponding values for the LRD-related parameter α of measured traffic. As a result, we found that the characteristic of self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurement locations. In training the self-organizing map, this paper used three parameters: two α values for different plot ranges, and Shannon-based entropy, which reflects the degree of concentration of measured time-series data. We visually confirmed that the traffic data could be projected onto the map in accordance with the traffic properties, resulting in a combined depiction of the effects of the degree of LRD and network utilization rates. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the projected data positions give us an effective depiction of network conditions at different times.