A Clustering Algorithm Based On Density An Distance And Its Application In Engineering

Nowadays,the procedure of residents going to the hospital is more and more complicated,and the simple data measurement is becoming also complicated.It is necessary to make a queue in advance to consult the doctor,which causes many unnecessary troubles to the residents.Society is at the stage of rapid information growth,especially in the field of medicine,the data is endless.As the amount of data increases,people usually store the data.Therefore,the concept of big data also attracts the attention of clustering analysis,which is a technique for finding hidden similarities of seemingly irregular data and classifying them into one class.The most commonly used clustering algorithms are based on density and distance.After many years of research,many scholars have improved them to some extent.Aiming at the above problems at present,a medical examination system of "The health" is designed,which solves the problem that the procedure of seeing a doctor is cumbersome and the health control is not timely.Through the human body information detection,to provide users with better treatment advice.In addition,for the current clustering algorithm,this paper proposes a clustering algorithm based on the combination of density and distance.The data analysis module uses the improved clustering algorithm to process the data.The specific work is as follows:(1)In this paper,we design and develop the "The health" system in factory mode based on reflection mechanism,and apply configurable management service and multilevel role permission mechanism to the system.The design of the system is completed by summarizing the whole system,introducing the related technology,analyzing the specific requirements and designing the database.The platform used by the system to store user data produces a lot of data to be processed,so the improved clustering algorithm is used to process these data.It can provide doctors with more accurate information,can provide doctors with more convenient and efficient information,so that users and doctors can establish a faster communication relationship.(2)in this paper,the density and distance based clustering algorithms are deeply studied.In view of the advantages and disadvantages of these two clustering algorithms,a clustering algorithm combining density and distance is proposed to automatically select the center points.The traditional algorithm uses a single attribute to measure the data.Distance and density are regarded as the two most important attributes of the center point.The test of the open data sets proves that the improved algorithm has a remarkable improvement in accuracy.In the part of data analysis of resident health housekeeper,the improved algorithm is used to deal with the user data,and a better clustering result is obtained.Through the analysis of the central data users,more accurate information is sent to the doctor,which has a better value.