Evaluating clustering quality using features salience: a promising approach

Neural Computing and Applications - This paper focuses on using feature salience to evaluate the quality of a partition when dealing with hard clustering. It is based on the hypothesis that a good...     

A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research

The objective of this paper is to propose a new unsupervised incremental approach in order to follow the evolution of research themes for a given scientific discipline in terms of emergence or decline. Such behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspects of information by opening the way to the detection of convergences and divergences of research themes at a large scale.     

An overview of the history of Science of Science in China based on the use of bibliographic and citation data: a new method of analysis based on clustering with feature maximization and contrast graphs

In the first part of this paper, we shall discuss the historical context of Science of Science both in China and at world level. In the second part, we use the unsupervised combination of GNG clustering with feature maximization metrics and associated contrast graphs to present an analysis of the contents of selected academic journal papers in Science of Science in China and the construction of an overall map of the research topics’ structure during the last 40 years. Furthermore, we highlight how the topics have evolved through analysis of publication dates and also use author information to clarify the topics’ content. The results obtained have been reviewed and approved by 3 leading experts in this field and interestingly show that Chinese Science of Science has gradually become mature in the last 40 years, evolving from the general nature of the discipline itself to related disciplines and their potential interactions, from qualitative analysis to quantitative and visual analysis, and from general research on the social function of science to its more specific economic function and strategic function studies. Consequently, the proposed novel method can be used without supervision, parameters and help from any external knowledge to obtain very clear and precise insights about the development of a scientific domain. The output of the topic extraction part of the method (clustering?+?feature maximization) is finally compared with the output of the well-known LDA approach by experts in the domain which serves to highlight the very clear superiority of the proposed approach.

     

Editorial

Neural Computing and Applications -     

Analysis of the dynamics and influence of the research work of Prof. Liu Zeyuan in China featuring a new hybrid approach combining community detection with topic tracking

Scientometrics - The influence of authors is mostly based on their capacity to form specific self-contained and/or active research communities or topics while also inspiring fruitful spin-off...     

New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping

The information analysis process includes a cluster analysis or classification step associated with an expert validation of the results. In this paper, we propose new measures of Recall/Precision for estimating the quality of cluster analysis. These measures derive both from the Galois lattice theory and from the Information Retrieval (IR) domain. As opposed to classical measures of inertia, they present the main advantages to be both independent of the classification method and of the difference between the intrinsic dimension of the data and those of the clusters. We present two experiments on the basis of the MultiSOM model, which is an extension of Kohonen's SOM model, as a cluster analysis method. Our first experiment on patent data shows how our measures can be used to compare viewpoint-oriented classification methods, such as MultiSOM, with global cluster analysis method, such as WebSOM. Our second experiment, which takes part in the EICSTES EEC project, is an original Webometrics experiment that combines content and links classification starting from a large non-homogeneous set of web pages. This experiment highlights the fact that break-even points between our different measures of Recall/Precision can be used to determine an optimal number of clusters for web data classification. The content of the clusters obtained when using different break-even points are compared for determining the quality of the resulting maps. This revised version was published online in June 2006 with corrections to the Cover Date.     

Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach

We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.