Unsupervised segmentation of gray level Markov model textures with hierarchical self organizing maps

Cuticular hydrocarbons were extracted from workers of 63 different nests of five species of Tetramorium ants (Hymenoptera: Formicidae) from Austria, Hungary, and Spain. The GC-MS data were classified (data mining) by self-organizing maps (SOM). SOM neurons derived from primary neuron separation were subjected to hierarchical SOM (HSOM) and were grouped to neuron areas on the basis of vicinity in the hexagonal output grid. While primary neuron separation and HSOM resulted in classifications on a level more sensitive than species differences, neuron areas resulted in chemical phenotypes apparently of the order of species. These chemical phenotypes have implications for systematics: while the chemical phenotypes for T. ferox and T. moravicum correspond to morphological determination, in T. caespitum and T. impurum a total of six chemical phenotypes is found. Three hypotheses are discussed to explain this disparity between morphological and chemical classifications, including in particular the possibility of hybridization and the existence of cryptic species. Overall, the GC-MS profiles classified by SOM prove to be a practical alternative to morphological determination (T. ferox, T. moravicum) and indicate the need to revisit systematics (T. caespitum, T. impurum).

The application of hierarchical self organizing maps (HSOM) to the segmentation of cell migration images, obtained during high-content screening in molecular medicine, is described. The segmentation is critical to our larger project for developing methods for the automatic annotation of cell migration images. The HSOM appears to perform better than the conventional computer-vision methods of histogram thresholding, edge detection, and the newer techniques involving single-layer SOMs. However, the HSOM techniques have to be complemented by region-based techniques to improve the quality of the segmented images.

In this paper, we propose new variants of unsupervised and competitive learning algorithms designed to deal with temporal sequences. These algorithms combine features from Spiking Neural Networks (SNNs) and the advantages of the hierarchical self organizing map (HSOM). The first variant named Hierarchical Dynamic recurrent spiking self-organizing map (HD-RSSOM) is characterized by the integration of a temporal controller component to regulate the firing activity of the spiking neurons. The second variant is a hierarchical model which represents a multi-layer extension of HD-RSSOM model. The case study of the proposed HSOM variants is phonemes and words recognition in continuous speech. The applied HSOM variants serve as tools for developing intelligent systems and pursuing artificial intelligence applications.

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlusTM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

Availability of large full-text document collections in electronic form has created a need for tools and techniques that assist users in organizing these collections. Document clustering is one of the popular methods used for this purpose. The Self-organizing map (SOM), an unsupervised algorithm for clustering and topographic mapping, has shown promising results in this task. Most of the existing SOM techniques rely on a “bag of words” document representation. Each word in the document is considered as a separate feature, ignoring the word order. In this chapter we investigate the use of phrases rather than words as document features for document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as features in two different document clustering algorithms, self-organizing map (SOM) and hierarchical self-organizing map (HSOM). We present results of clustering documents from the REUTERS corpus and show an improvement in the clustering performance evaluated using the entropy and F-measure.

Automated security management has been a critical but difficult issue in the field of network research. Among various artificial-intelligence techniques, traditional learning techniques based on supervision are not suitable for automated network management, particularly in terms of detecting temporal changes in network intrusion patterns and characteristics, because supervised learning requires a manager. In this regard, unsupervised learning techniques such as the self-organizing map (SOM) are more suitable for automated network management in terms of their configuration, performance, and anomaly detection capability. This paper proposes an automated security management system based on a hierarchical SOM that groups similar data and visualizes their clusters. The proposed system labels maps produced by the SOM based on correlations between features for automated network management. The experimental results for the proposed system based on the 1999 KDD Cup data set indicate that the system shows reasonable misclassification rates and takes 0.5 second to determine whether a behavior is a normal one or an attack.

This paper proposes a new methodology which combines supervised learning, unsupervised learning and genetic algorithm for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised ANNs which perform an estimation of post-fault power system state. The technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a IEEE 9 bus power system are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation.

A robust and flexible model of hierarchical self-organizing maps for non-stationary environments

This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions.

Automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.

Approaches the texture segmentation problem by clustering feature vectors created from a Gabor transform data block. Given an N*N image, the authors compute 24 Gabor transforms using Gabor kernels with six orientations and four sizes. This results in a Gabor data block composed of N/sup 2/ feature vectors of length 24. The feature vectors are then grouped based on their distribution in the high-dimensional feature space. The authors hypothesize that the pixels in a given group have similar characteristics, and thus are part of the same texture. Experimental results for segmenting a synthetic railroad track image were encouraging; a clear-cut segmentation of the image was obtained. By clustering Gabor features, the authors were able to segment an image into regions of uniform texture without prior knowledge of the types of texture, or the frequency and orientation characteristics of these textures. The clustering algorithm is a modified Kohonen self-organizing feature map. >

The popularity of multimedia on the Internet has created a heavy load on bandwidth. Consequently, content compression and bandwidth reduction have become significant topics recently. An appropriate codebook design is an essential and valuable principle for Vector Quantization (VQ). This investigation presents a new image compression method called INTSOM, which relies on Hierarchical Self-Organizing Map (HSOM) and adopts LBG for speeding up. For a two-layer neural network, INTSOM first employs LBG to determine the number of first layer neurons, and uses an estimation function to determine the number of second layer neurons dynamically. A modified SOM is then performed to compress each sub-map. Experimental results indicate that INTSOM has better overall capability (time-cost and quality) than LBG, SOM and HSOM.Keywordsimage compressionvector quantizationLBGSOMHSOM

Content-based image retrieval using growing hierarchical self-organizing quadtree map

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

We have been working on the application of Machining Learning in Metabolomics, Drug-Drug Interaction Discovery and Human Gait Recognition [1-5], profiling large data sets. Extraction of vital information about 1) plant metabolomics that can improve the environment and food quality, 2) in vitro neuronal network behavior patterns for various drugs that can be used to characterize drugs for brain deceases and 3) human gait patterns that can reveal various diseases were among these applications. We have demonstrated with considerable success in using unsupervised clustering techniques to analyze genetic and metabolomic data. This includes analysis of drought resistance in wheat [4] and microbial metagenomes [5].We introduced two methods: Near Unsupervised Learning (NUL) and Sub-sample Error Graphs (SEGs) [5] to analyze large amount of data. Self Organizing Map (SOM), which is one of the widely used Unsupervised Neural Networks, has been used as a data-mining tool due to its ability to map high dimensional data into a two dimensional feature map, which is expected to be topology preserving allowing users to visually identify clusters by their topological relationships in terms of their proximity on the map. The Growing Self Organizing Maps (GSOM) further allows the map size to be determined by the algorithm, which relies upon a user set parameter called Spread Factor (SF) [6]. The wide availability of GPUs for affordable prices allows faster comparison of various SOMs with different maps sizes taking away some of the advantages GSOM claimed. Further development of GSOM into a Dynamic SOM Tree exploits the possibility of varying SF to obtain multiple GSOMs from a small number of compact clusters to a large number of sparse clusters [7]. NUL methods can be applied on GSOM using a small number of labels that should be available for every class. This is however not realistic in some applications where the number of classes cannot be explicitly known. We propose a new method call Deep Near Unsupervised Learning (D-NUL), where Dynamic SOM tree is used instead of GSOM and the number of classes are not assumed to be known. The implementation of Dynamic SOM tree methods with varying SF on GPUs will make the computation with D-NUL possible for many problems in big data analytics.