Economic Management Data Envelopes Based on the Clustering of Incomplete Data

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

In this paper, the economic management data envelope is analyzed by an algorithm for clustering incomplete data, a local search method based on reference vectors is designed in the algorithm to improve the accuracy of the algorithm, and a final solution selection method based on integrated clustering is proposed to obtain the final clustering results from the last generation of the solution set. The proposed algorithm and various aspects of it are tested in comparison using benchmark datasets and other comparison algorithms. A time-series domain partitioning method based on fuzzy mean clustering and information granulation is proposed, and a time series prediction method is proposed based on the domain partitioning results. Firstly, the fuzzy mean clustering method is applied to initially divide the theoretical domain of the time series, and then, the optimization algorithm of the theoretical domain division based on information granulation is proposed. It combines the clustering algorithm and the information granulation method to divide the theoretical domain and improves the accuracy and interpretability of sample data division. This article builds an overview of data warehouse, data integration, and rule engine. It introduces the business data integration of the economic management information system data warehouse and the data warehouse model design, taking tax as an example. The fuzzy prediction method of time series is given for the results of the theoretical domain division after the granulation of time-series information, which transforms the precise time-series data into a time series composed of semantic values conforming to human cognitive forms. It describes the dynamic evolution process of time series by constructing the fuzzy logical relations to these semantic values to obtain their fuzzy change rules and make predictions, which improves the comprehensibility of prediction results. Finally, the prediction experiments are conducted on the weighted stock price index dataset, and the experimental results show that applying the proposed time-series information granulation method for time series prediction can improve the accuracy of the prediction results.

Related collections

Most cited references 20

Record: found
Abstract: found
Article: not found

Watering Down Environmental Regulation in China*

Guojun He, Shaoda Wang, Bing Zhang (2020)

This article estimates the effect of environmental regulation on firm productivity using a spatial regression discontinuity design implicit in China's water quality monitoring system. Because water quality readings are important for political evaluations and the monitoring stations only capture emissions from their upstream regions, local government officials are incentivized to enforce tighter environmental standards on firms immediately upstream of a monitoring station, rather than those immediately downstream. Exploiting this discontinuity in regulation stringency with novel firm-level geocoded emission and production data sets, we find that immediate upstream polluters face a more than 24% reduction in total factor productivity (TFP), and a more than 57% reduction in chemical oxygen demand emissions, as compared with their immediate downstream counterparts. We find that the discontinuity in TFP does not exist in nonpolluting industries, only emerged after the government explicitly linked political promotion to water quality readings, and was predominantly driven by prefectural cities with career-driven leaders. Linking the TFP estimate with the emission estimate, a back-of-the-envelope calculation indicates that China's water regulation efforts between 2000 and 2007 were associated with an economic cost of more than 800 billion Chinese yuan.

0 comments Cited 65 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography

Yu Zhang, Wei. Wu, Russell T. Toll … (2020)

The understanding and treatment of psychiatric disorders with neurobiological and clinical heterogeneity could benefit from the identification of disease subtypes on the basis of data acquired with established neuroimaging technologies. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional-connectivity patterns, prominently within the frontoparietal-control and default-mode networks. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets from patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.