Study On Attribute Reduction Of Rough Set And Clustering Algorithm And Their Application In Power Automation System

Data mining is an innovated method of data analysis. It can help people maximize the useful information included in tremendous data, which has become active in artificial intelligence field. Rough set theory is a theory adopted to deal with rough and uncertain knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available, providing a new method for data classification. Although there are numerous methods of rough set and cluster analysis, as the data objects is changing continuously, we have to improve these relevant technologies over time, and propose creative theory in response, meeting the demands of application.This paper proposes the basic conceptions and attributes of relevant influences in rough set and study the interactions between different attributes, presenting a attributes reduction algorithm based on relevant influences. Through the matrix of relevant influences of attributes, making the relevant influences of attributes as inspiring prerequisite, we effectively delete redundant attributes to gain the reduced sets which reflects the interaction of different attributes. As proved by experiments, the algorithm could obtain the reduction sets composed of attributes with high relevant influences. This conception expands the application range of rough set, presenting a new method for data mining.Based on the conceptions of relevant influences of rough set, we study dynamic reduction conceptions and methods on the basis of relevant influenced attributes, and calculate the effects of activation stateρ(U) and dormancy stateσ(U) in rough set samples on the attributes reduction sets, whenρ(U)→σ(U), reduce the redundant attributes from reduction sets, whileσ(U)→ρ(U), add the indispensible attributes to the reduction sets, enabling the exchanges of event states be described more effectively by rough sets. This method compensates the deficiency of the previous methods that rough set could only describe static objects.As intelligence supervising system is the core of industrial automatic control, rough set theory has provided practicable real time decision principles, deducting the weak real time attributes and retaining strong real time ones, to ensure the real time principle of the decision system. The real time method of attributes reduction proposed by this paper has expanded the application of rough set in real time decision systems. As to the requirements of classification of conditional attributes in decision tables, this paper proposes a reduction algorithm on the basis of attributes classification, which first conducts classification calculation on conditional attributes according to classification functions, then deletes the less important subsets, concluding the classified reduction sets of attributes. As experiments have proved, maintaining the original decision ability constant, this algorithm could deduct parts of the attributes effectively and solves the attributes classification problems.We have applied our algorithm of attributes classification to the failure diagnosis of electric distribution network and early warning systems of electric interlock network. Studied the attributes choices and rules generation methods of failure diagnosis of electric distribution network, we employed real time attributes reduction and attributes classification reduction to the failure diagnosis systems of electric distribution network. Through calculation of the values of attributes of failure diagnosis and early warning systems of electric interlock network on the basis of electric theory, we have studied the relevant interactions between attributes under the condition of negative charges transforming, obtaining the extents of failures in the network. By the application of reduction algorithm of relevantly influenced attributes, we have observed the changes of attributes and achieved our goal of predicting failures and getting them fixed in time.Cluster/classification congruity algorithm, proposed in the situation of inconsistence between classification and cluster, is a method to achieve the results the accordance of cluster and classification respectively after calculates the congruity matrix, and coordinates the results effectively by modifying the matrix continuously, in order to achieve the maximum congruity. In the application of prediction of negative charges, this algorithm has a wide application, which could be used on some occasions when the results of cluster and classification are not consistent.These algorithm mainly conducts research on algorithms of attributes reduction and cluster analysis in rough sets, proposing several creative theories and methods, which also have been applied to electric automatic systems as experiments. As have been proved by experiments, these theories and methods are effective and practicable.