Data Mining Algorithms Improvement In Decision Support Systems Based On Information Entropy

Data mining act as a decision support technology plays an important role in thedecision support system, which has closely linked with the amount of data,organization, and organizational structure. The data mining methods try to find therelevance of knowledge and show the hidden structure information of the data,which will provide users with more accurate information for decision making. Datamining has been widely applied and its decision support effect is more obvious.To use data mining technology build the institutional mechanisms ofinformation decision support system based on information entropy theory. Usinginformation entropy can measure the degree of regularity of informationorganization in decision support system. Information entropy of the data is based onuncertainty analysis and judgment. The larger entropy reflects the state of disorder ofthe organizational structure of the data in the system. In contrast, the smaller entropyreflects the orderly of organizational structure of the data in the system, and data andspace utilization will reach the maximum and processing complexity and cost of thesystem has been the lowest. In this study, the enterprise decision support system isthe starting point, the uncertainty analysis of information and data by the entropy isthe measurement of the degree of uncertainty in the data. Using ant colony clusteringalgorithm and information entropy theory analyze customer data on the network.Uncertainty analysis of information entropy theory can better help the clustering ofdata objects and improve the effectiveness of decision-making.Ant colony mining based on information entropy algorithms can effectively sortthe importance degree of data and information to abandon some data or dataorganization scheme that cause greater deviation or reduce system efficiency, inorder to reconstruct the original data and reach the more objective results fordecision-making.