Research Of Wargaming Data Analysis Methods Based On Data Mining

In peacetime, the computer wargame is one of the most suitable tools for higherlevels of military command training due to its superiority in terms of money, amount ofsubordinate troops required, land use and approximative realistic experience. And itsdevelopment is receiving increased emphasis. As an important component of thecomputer wargame, analyzing wargaming data can help commanders to findstrongpoints or weakness exploited in the wargaming process, improving its effect inmilitary command training and operation theories exploration. At the same time,analyzing wargaming data is also a difficult problem because of that the computerwargame possesses such characteristics as large-scope, multi-scale, high complexity,and so on. It is under such background that the author effectively studies thecorresponding martial requirements of the computer wargame and proposes some dataanalysis methods based on data mining, the achievements of this dissertation can beconcluded as follows:(1) Considing the complex structure and high running performance of the computerwargame as well as the diversity and massiveness of wargaming data, based on studyingthe characters of the wargaming data, the author describes three kinds of wargamingdata structure, proposes data collection principles, defines seven basic collection modes,and then analyses forth and compares the merits and defects of current main datacollection methods and technologies and their applicability according to those definedcollection modes, which meets data collection requiements at various developmentstages. Besides that, to solve some practical problems such as efficient data storageneeds in the development and applications of the computer wargame, we provide threedata storage policies based on files and their efficiencies of access and storage arecomparatively analysed using the spatial-temporal complexity computing method.(2) A clustering algorithm named QDBSCAN is proposed for determining thevulnerability of units’ deployment rapidly by detecting the isolated units in order to savethe time consumed by commaders for analysing the battlefield situation in thewargaming process. Compared with DBSCAN, QDBSCAN made some improvementsin such aspects:①defined the shortest viable path as the similarity measurement tomake the clustering algorithm more coincident with the rules of computer wargames;②set the density parameters dynamically instead of statically;③chose a small numberof representative objects to expand the cluster so that the execution frequency of regionquery was reduced and grouped the whole dataset by divisiory regions in order toreduce the scale of clustering and ulteriorly enhance the efficiency of the algorithm.Experimental results indicate that QDBSCAN is more effective and efficient thanDBSCAN in clustering large datasets. (3) A hotspot detection algorithm based on connected tree is proposed, which iscapable of detecting arbitrarily shaped hotspots during the wargaming process. Bydetecting the areas with high concentrations of martial events, this algorithm couldassist commaders understanding the whole wargaming battlefield situation andconjecturing the intent of opponents. After making the definition of a hotspot, aconnected tree is built in order to least divide the whole dataset into connected regions,and a pruning procedure is carried out according to the provided density threshold value.Each pruned connected subtree is a hotspot which we would like to acquire. Throughadjusting the density threshold, commanders with different levels can chooseappropriate observational views. Both the theoretical analysis and experimental resultsverify the effectiveness of the algorithm.(4) By clustering trajectories of the wargame entities, we may find their holisticbehavioral patterns which could help commanders estimate the intent of their opponents.Thereby, in allusion to the characters of trajectory data of the wargame entities atrajectory clustering algorithm named CTECW is proposed. This algorithm is composedof three parts: trajectory pretreatment, trajectory segments clustering and visualpresentation. Trajectory pretreatment transforms original trajectories into simplifiedones which are ulteriorly processed into linear segments. In the second part, the conceptof density function derived from DENCLUE is introduced and trajectory segments areclustered based on our own similarity measure under the framework of DBSCAN.Moreover, how to choose optimal parameters is discussed detailedly. Visualpresentation exhibits clustering results with martial meanings which can be easilycomprehended by commanders. Both the theoretical analysis and experimental resultsverify the effectiveness of the algorithm.(5) After analysing the development of the data mining system as well asarchitectures and functions of main data mining systems, a wargame data analysisprototype system based on data mining is designed.