The Design And Implement An Increment Grain Company Information Cluster System

What have been realized in this article is a part of "Intelligent Decision Support System of Grain"(IDSSG), which is one of main technological projects supported by JiLin technology department. The system is on the background of actual work of grain enterprises in JiLin province. It used many ways such as Data-Warehouse, Data-Mining, Statistic analysis , acknowledge illation and so on to establish the Grain Data-Warehouse that concerned to every grain business steps, which involved grain purchasing, grain transportation, grain rotating, grain scheduling, making grain report forms , data inquiring and analysing. The system established six models on the base of Grain Data-Warehouse , these modules are multidimensional data analysis model, grain-keeping decision support model , grain-rotating decision support model, grain-scheduling decision support model, grain company information cluster analysis model and grain pre-warning model. What will be talk about is the grain company information cluster analysis model.In this article , I used cluster analysis ways of Data-Mining to cluster on every kinds of grain company information , which involved natural information (grain depot area , number of employee and so on) and business information (purchase number of a year , drying number of a year , rotating number of a year and so on), which can divide all the data items into several groups and there are great difference between groups but characteristic of a group is obvious. So customers can make analysis on this cluster result to get some information about all the grain companies' development status. They can get these information in this model: grain companies' classify status, detailed information of every class of grain company, geography distributing of each class , and can also know which information item has more influence on the classification. And these information can provide decision support for grain company leaders on summarizing the effect of grain policy, making or adjusting grain business tactic and making different policies on different area. Level cluster algorithm and split cluster algorithm were made use of in the system, and they are distance-based. Level cluster algorithm is a converge-style one, in every cluster cycle the program will select two data items whose distance is nearest and converge them into one group. It will be done like this until all the data items converge into one group or the group number reaches what customers have appointed before hand. This algorithm computes exactly and customer can analysis its cluster process to know about the structure of all the data items. Split cluster algorithm is also called K-Means cluster algorithm. It computer more rapidly than Level cluster algorithm but its result is not precise enough sometimes because it will be influenced by some parameters , for example the selection of the initial center point . So actually these two algorithms often be selected according to the data number and customer's require for the cluster result . In the state that data number is small level cluster algorithm can be used , but Grain Data-Warehouse's data number often is very large , so level cluster algorithm can be used firstly to get the level cluster process , then analysising it to get the appropriate result number . So customer can use split cluster algorithm with this result number in sometime later . Some important questions are discussed in this article such as how to compute the distance between data items , how to process the exceptional data items , how to realize the limit conditions of cluster and how to select the initial center points of split cluster algorithm and so on .Grain Data-Warehouse has a large data number and frequently update rate, so only use level cluster algorithm and split cluster algorithm can not get a good result.And in many times the results got in former clusters can't be used . So a increment cluster algorithm was brought forward and realized in this article . This algorithm do cluster on the base of a former...