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.
Highlights
Clustering is an unsupervised data mining method. e basic idea is to measure the similarity between the data based on the intrinsic properties of the data and classify the samples with greater similarity into the same class and those with less similarity into different classes [1]
Since the features of different classes of data may correspond to different feature subspaces, and the feature dimensions composing these feature subspaces
The data in the data warehouse comes from various data sources, including various heterogeneous database systems, data file data, other data, etc
Summary
Clustering is an unsupervised data mining method. e basic idea is to measure the similarity between the data based on the intrinsic properties of the data and classify the samples with greater similarity into the same class and those with less similarity into different classes [1]. Clustering is an unsupervised data mining method. E basic idea is to measure the similarity between the data based on the intrinsic properties of the data and classify the samples with greater similarity into the same class and those with less similarity into different classes [1]. E presence of a large amount of noise and redundant features makes it very unlikely that clusters exist in all dimensions. As the sample dimensionality increases, the distance difference between the samples becomes smaller, and the data becomes sparse in the high-dimensional space [2]. E subspace clustering methods follow this idea and seek to identify the different classes of clusters in different feature subspaces in the same dataset. Since the features of different classes of data may correspond to different feature subspaces, and the feature dimensions composing these feature subspaces
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