Double Clustering Based Neural Feedback Method for Unstructured Text Data

Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.

Bibliometric Analysis of Studies on Tumor Organoids

Background: Organoid is an artificially grown mass of cells or tissues, which is similar to an organ. It can replicate the complexity of an organ and can be used for gaining a better understanding of diseases. In this study, the hot spots of “organoids” were classified into 6 categories and 10 aspects, and organoids used for studying genetic mechanisms, drug effect, and metabolism of tumors showed the greatest potential for future development.Methods: A total of 1550 articles relevant to organoid in tumor research field were recruited as research samples. High-frequency words and text/co-word matrix were constructed by BICOMB software. gCLUTO software was applied to analyze the matrix by double-clustering and visual analysis subsequently to identify the hotspot in this area.Results: We constructed a text and co-word matrix composed of 21 high-frequency words and 1550 articles and generated a hotspot “peak map” based on double-clustering analysis. The strategic coordinates approach was used to assess the research prospects of each hotspot and the connections between these hotspots.Conclusions: In this study, we classified the hot-spots of “organoid” into 6 categories and 10 aspects. Calculation and analysis revealed that the field of tumor organoid shows a slight trend of polarization, and organoid for studying the genetic mechanisms, drug effects and metabolism of tumor shows the greatest potential for future development.

A double clustering algorithm for financial time series based on extreme events

This paper is concerned with a procedure for financial time series clustering, aimed at creating groups of time series characterized by similar behavior with regard to extreme events. The core of our proposal is a double clustering procedure: the former is based on the lower tail dependence of all the possible pairs of time series, the latter on the upper tail dependence. Tail dependence coefficients are estimated with copula functions. The final goal is to exploit the two clustering solutions in an algorithm designed to create a portfolio that maximizes the probability of joint positive extreme returns while minimizing the risk of joint negative extreme returns. In financial crisis scenarios, such a portfolio is expected to outperform portfolios generated by the traditional methods. We describe the results of a simulation study and, finally, we apply the procedure to a dataset composed of the 50 assets included in the EUROSTOXX index.