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

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).

Download Full-text

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

Integrations of Data Warehousing, Data Mining and Database Technologies ◽

10.4018/978-1-60960-537-7.ch013 ◽

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).

Download Full-text

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

Journal of Robotics ◽

10.1155/2010/860790 ◽

2010 ◽

Vol 2010 ◽

pp. 1-9 ◽

Cited By ~ 3

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.

Download Full-text

Document Clustering by Semantic Smoothing and Dynamic Growing Cell Structure (DynGCS) for Biomedical Literature

Data Warehousing and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/978-3-540-85836-2_21 ◽

2008 ◽

pp. 217-226

Author(s):

Min Song ◽

Xiaohua Hu ◽

Illhoi Yoo ◽

Eric Koppel

Keyword(s):

Cell Structure ◽

Document Clustering ◽

Biomedical Literature ◽

Growing Cell

Download Full-text

Document Clustering Based on Vector Quantization and Growing-Cell Structure

Developments in Applied Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/3-540-45034-3_33 ◽

2007 ◽

pp. 326-336 ◽

Cited By ~ 3

Author(s):

Zhong Su ◽

Li Zhang ◽

Yue Pan

Keyword(s):

Vector Quantization ◽

Cell Structure ◽

Document Clustering ◽

Growing Cell

Download Full-text

A SOM PROJECTION TECHNIQUE WITH THE GROWING STRUCTURE FOR VISUALIZING HIGH-DIMENSIONAL DATA

International Journal of Neural Systems ◽

10.1142/s0129065703001662 ◽

2003 ◽

Vol 13 (05) ◽

pp. 353-365 ◽

Cited By ~ 8

Author(s):

ZHENG WU ◽

GARY G. YEN

Keyword(s):

Projection Method ◽

Cell Structure ◽

High Dimensional Data ◽

Network Size ◽

High Dimensional ◽

Data Sets ◽

Self Organizing Map ◽

Data Set ◽

Growing Cell ◽

Self Organizing

The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy visualization of the data. The projection method is demonstrated on four different data sets, including a 118 patent data set and a 399 checical abstract data set related to polymer cements, with promising results and a significantly reduced network size.

Download Full-text

Rule Insertion Technique for Dynamic Cell Structure Neural Network

International Journal of Neural Networks and Advanced Applications ◽

10.46300/91016.2020.7.3 ◽

2020 ◽

Vol 7 ◽

Keyword(s):

Neural Network ◽

Flight Control ◽

Expert Knowledge ◽

Cell Structure ◽

Training Data ◽

Self Organizing Map ◽

Data Set ◽

Adaptive Flight Control ◽

Dynamic Cell ◽

Iris Data

This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Download Full-text