Relation organization of som initial map by node exchange with fixed neighbor areas

Self Organizing Map (SOM) is a kind of neural networks, that learns the feature of input data thorough unsupervised and competitive neighborhood learning. In SOM learning algorithm, every connection weights in SOM feature map are initialized to random values to covers whole space of input data, however, this is also set nodes to random point of SOM feature map independently with data space. The move distance of output nodes increases and learning convergence becomes slow for this. To improve SOM learning speed, here I propose a new method, node exchange of initial SOM feature map, and a new measure of convergence, the average of the move distance of nodes. As a result of experiments, the average of the move distance of nodes comes to short that it becomes about 45%, and learning speed is improved that it becomes about 50% by this method.

In Kohonen’s Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM’s architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm.

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.

An investigation of local energy surface detection integrated with neural network techniques for image segmentation is presented, as applied in the feature extraction of chromosomes from image datasets obtained using an experimental confocal microscope. Use of the confocal microscope enables biologists to observe dividing cells (living or preserved) within a three-dimensional (3-D) volume, that can be visualised from multiple aspects, allowing for increased structural insight. The Nomarski differential interference contrast mode used for imaging translucent specimens, such as chromosomes, produces images not suitable for volume rendering. Segmentation of the chromosomes from this data is, thus, necessary. A neural network based on competitive learning, known as Kohonen's self-organizing feature map (SOFM) was used to perform segmentation, using a collection of statistics or features defining the image. Our past investigation showed that standard features such as the localized mean and variance of pixel intensities provided reasonable extraction of objects such as mitotic chromosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigated as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is applied in a SOFM, producing 3-D images exhibiting vast improvement in the level of detail and clearly isolating the chromosomes from the background. Index Terms-DIC, differential interference contrast, feature extraction, feature space, image segmentation, local energy, Morlet wavelet, phase congruency, self organizing feature map, SOFM.

Ability to organize data spatially while conserving the topological relation between data features makes the Self Organizing Map (SOM) a very useful tool for analysis and visualization of high dimensional data such as a power transformer's Dissolved Gas Analysis (DGA). Past SOM application required large historical data for its training and has limited fault detection sensitivity. In this paper, the effects of input features and data normalization are studied to enhance SOM's clustering. SOM is trained using DGA results extracted from actual faulted transformers. Combination of input features and data normalization methods are tested on SOM before the best SOM is identified. Validation is conducted using several datasets i.e. the IEC Technical Committee 10 database. Compared with past SOM applications, the proposed SOM required lesser training data, improved SOM's sensitivity in incipient fault detection and has good diagnosis accuracy. The proposed SOM is also compared with other AI-based DGA interpretation method i.e. Support Vector Machine (SVM) for benchmarking.

Summary Well-test data have been used traditionally for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, and reservoir mechanism. A number of techniques, both conventional methods, such as type-curve matching and numerical simulation, and artificial intelligence (AI) methods, have been used for identifying well-test models. These methods are laborious and time-consuming and at times give incorrect results. Artificial neural networks (ANN's) are recent developments in computer vision and image analysis. These networks are specialized computer software that generate a strategy to produce nonlinear mapping functions for complex problems. ANN's are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well-test data have been reported. These applications are mostly model-specific (developed for specific reservoir models) and, hence, are not general enough. This paper presents a new method based on ANN's that uses Ko-honen's self-organizing feature (SOF) mapping technique to identify well-test interpretation models. By grouping well-test data into distinct categories, the SOF algorithm produces a general mapping function. This method can help analyze well-test data from a large variety of reservoirs (including reservoirs with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was feasible previously.

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.

This paper presents a comparison of competitive learning algorithms for Self Organizing Map (SOM). The competitive learning algorithms showed to self organizing map algorithm are winner-takes-all, Frequency Sensitive Competitive Learning and Rival Penalized Competitive Learning. The result shows the performance in classification of partial discharge on power cables using SOM.

Indonesia merupakan negara yang mengandalkan hasil dari sektor pertanian dan sektor perkebunan sebagai sumber mata pencaharian dan penopang pembangunan. Peningkatan dan pemeliharaan produktivitas serta efisiensi sektor perkebunan di suatu wilayah dapat dilakukan apabila pemerintah daerah dapat mengetahui potensi daerahnya tersebut. Untuk mengetahui potensi komoditas sektor perkebunan di Indonesia, perlu diketahui hasil clustering dengan algoritma Self Organizing Map (SOM). SOM merupakan algoritma yang melakukan pemetaan dari data yang ada di ruang vektor berdimensi tinggi ke ruang vektor dua dimensi yang terletak pada lokasi yang berdekatan. Selama proses, neuron yang paling cocok dengan pola input dipilih sebagai winner neuron. Analisis clustering akan menggunakan data produksi perkebunan di Indonesia tahun 2020. Pembentukan cluster diawali dengan normalisasi data, kemudian melakukan proses iterasi untuk mendapatkan neuron. Selanjutnya dapat menetukan inter-cluster dan menentukan yang terkecil. Hasil cluster yang terbentuk dilakukan proses validasi menggunakan DBI. Hasil penelitian menghasilkan 5 cluster dengan nilai DBI 0,2039. Setiap cluster yang terbentuk memiliki karakteristik tersendiri diantaranya cluster 1 memiliki hasil produksi perkebunan kakao tertinggi, cluster 2 memiliki nilai tertinggi pada hasil produksi perkebunan kelapa, tebu, dan tembakau, cluster 3 memiliki nilai tertinggi pada hasil produksi perkebunan karet dan kopi, cluster 4 memiliki hasil produksi perkebunan kelapa sawit tertinggi, dan cluster 5 hasil produksi perkebunan teh tertinggi.Kata kunci: perkebunan, Self Organizing Map, clustering¸ Davies Bouldin Index.

ICSSSS: An intelligent channel selection scheme for cognitive radio ad hoc networks using a self organized map followed by simple segregation

Application of Self Organizing Map and SRTM data to characterize yardangs in the Lut desert, Iran

Electromyography (EMG) is a bio signal that is formed by physiological variations in the state of muscle fibre membranes. Pattern recognition is one of the fields in the bio-signal processing which classified the signal into certain desired categories with subject to their area of application. This study described the classification of the EMG signal based on human body percentile using Self Organizing Mapping (SOM) technique. Different human percentile definitively varies the arm circumference size. Variation of arm circumference is due to fatty tissue that lay between active muscle and skin. Generally the fatty tissue would decrease the overall amplitude of the EMG signal. Data collection is conducted randomly with fifteen subjects that have numerous percentiles using non-invasive technique at Biceps Brachii muscle. The signals are then going through filtering process to prepare them for the next stage. Then, five well known time domain feature extraction methods are applied to the signal before the classification process. Self Organizing Map (SOM) technique is used as a classifier to discriminate between the human percentiles. Result shows that SOM is capable in clustering the EMG signal to the desired human percentile categories by optimizing the neurons of the technique.

Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering

Self organized mapping of data clusters to neuron groups

This paper presents a multi agent system which provides a method for cooperation and coordination among agents using collective reasoning. There are two types of agents in the system: distributed agents and a central administrator agent. The distributed agents provide local knowledge extraction tasks and communicate it to the central administrator agent. The central administrator agent builds a global picture about the problem domain using the local knowledge of each agent. It uses the Growing Self Organizing Map (GSOM), which is a dynamic version of the Self Organizing Map (SOM) for analyzing the global knowledge. The system differs from similar systems which employ central administrator functionality as the central administrator agent in the proposed system is capable of autonomously refining the task model of the distributed agents and dynamically determining the efficient number of agents required in the system.