A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2011 ◽  
pp. 307-319
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
The Neural Network ◽  
Structure Algorithm

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this paper, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2009 ◽  
Vol 5 (4) ◽  
pp. 44-57 ◽  
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
Document Models ◽  
Clustering Quality

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

scholarly journals Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Magnus Johnsson ◽  
Christian Balkenius
Keyword(s):  
Neural Network ◽  
Haptic Perception ◽  
Cell Structure ◽  
The Self ◽  
Robot Hand ◽  
Self Organizing Map ◽  
Training Set ◽  
Input Space ◽  
Growing Cell ◽  
Self Organizing

We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self-organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

Credit Scoring Using Supervised and Unsupervised Neural Networks

2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Scoring ◽  
Self Organizing Map ◽  
Subprime Lending ◽  
The Neural Network ◽  
Combined Use

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.

FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 233-256 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

scholarly journals Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 299
Author(s):  
Birgitta Dresp-Langley ◽  
John M. Wandeto
Keyword(s):  
Neural Network ◽  
Mirror Symmetry ◽  
Image Data ◽  
Choice Response ◽  
Local Contrast ◽  
Self Organizing Map ◽  
The Neural Network ◽  
Unsupervised Neural Network ◽  
Ambiguous Data ◽  
Self Organizing

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self-Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data.

scholarly journals IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)

2017 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Fatma Agus Setyanngsih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Self Organizing Map ◽  
Rainfall Prediction ◽  
The Neural Network ◽  
Network Time ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
The City ◽  
Self Organizing

<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 124 datas, with MSE value 0,0148.</em><strong> </strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>Rainfall, Neural Network, Time Series, Self Organizing Map</em></p><p><em>Prediksi untuk mengetahui curah hujan yang terjadi di Pontianak sangat dibutuhkan salah satunya yaitu menggunakan algoritma jaringan syaraf tiruan dengan pengelompokkannya menggunakan SOM (Self Organizing Map) dengan data yang digunakan adalah data di bulan januari tahun 2010-2013. Tujuan dari penelitian ini adalah untuk mengetahui prediksi curah hujan di kota Pontianak dengan parameter suhu udara, kelembababn relative, tekanan udara dan kecepatan angin. Hasil penelitian menunjukkan bahwa nilai MSE ini didapatkan saat jaringan mempelajari data prediksi pada bulan januari di tahun 2010 sampai tahun 2013 dengan menggunakan proses pembelajaran JST SOM dengan jumlah neuron 1 dan menggunakan 124 data, dengan nilai MSE 0,0148. </em></p><p><em></em><em><strong><em>Kata kunci</em></strong><strong><em>:</em></strong><em> </em><em>Curah Hujan, Jaringan Syaraf Tiruan, Time Series, Self Organizing Map</em></em></p>

scholarly journals Using Artificial Neural Network for Multimedia Information Retrieval

2019 ◽  
Vol 54 (3) ◽  
Author(s):  
Maha Mahmood ◽  
Wijdan Jaber AL-kubaisy ◽  
Belal Al-Khateeb
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Multimedia Information ◽  
Query Language ◽  
Human Perception ◽  
Multimedia Data ◽  
The Other ◽  
Discrete Wavelet ◽  
Multimedia Information Retrieval ◽  
The Neural Network

Multimedia Information Retrieval (MIR) is an important field due to the great amount of information going through the Internet. Multimedia data can be considered as raw data or the features that compose it. Raw multimedia data consists of data structures with diverse characteristics such as image, audio, video, and text. The big challenge of MIR is a semantic gap, which is the difference between the human perception of a concept and how it can be represented using a machine-level language. The aim of this paper is to use different algorithms through two stages one for training and the other for testing. The first algorithm depends on the nature of the query language to retrieve the text document using two models, Vector Space Model (VSM) and Latent Semantic Index (LSI). The second algorithm is based on the extracted features using curvelet decomposition and the statistic parameters such as mean, standard deviation and energy of signals. The other algorithm is based on the discrete wavelet transform (DWT) and features of signals to retrieve audio signals, then the neural network is applied to describe the information retrieval model which retrieves the information from the multimedia. The neural network model, based on multiplayer perceptron and spreading activation network type, accepts the structure of conceptually and linguistically oriented model.

Line Segment-Based Clustering Approach With Self-Organizing Maps

2021 ◽  
Vol 14 (4) ◽  
pp. 33-44
Author(s):  
G. Chamundeswari ◽  
G. P. S. Varma ◽  
C. Satyanarayana
Keyword(s):  
Neural Network ◽  
Line Segment ◽  
Feature Vector ◽  
Input Image ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Clustering Techniques ◽  
Hough Transformation ◽  
The Neural Network ◽  
Self Organizing

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

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