Research And Application Of Data Analysis Of Archaeological Excavations

With the development of archaeological activities, more and more digital resources come out into our sight from archaeological excavations. However, much of them are redundant, which bring much inconvenience to the research of archaeologists. As an important part of data mining, data generalization can help to simplify them and efficiently improve the quality of related work of archaeologists.Firstly, we deeply study the basic theory of data generalization based on attribute oriented induction and use archaeological data of LiangZhu cultural site to explain its application in the area of archaeological resource analysis. Because of the occasion that thresholds must be set manually and in detail, I come up with another algorithm to generalize data so as to avoid setting thresholds oneself. To eliminate invalid generated rules, I use the support value of the rules to filter invalid ones. I conduct several experiments to test the impact of the two algorithms. Results show that algorithms have achieved good performance. At last of that part, I analyze time complexity and space complexity of algorithms.On the other hand, we think out the other way to do data generalization based on clustering. We finish the experiment of agglomerative algorithm to do clustering on the data of LiangZhu cultural site. We get more concise results than source data. But more time is taken meanwhile. We compare different aspects of the two algorithms above in detail. Besides, we also visualize results in a more clear way, such as using pie chart or bar chart.Finally, we describe the framework and implementation of the excavated information management system. The system integrates different functional parts of data analysis, including data integration, management, analysis and so on. Data generalization is implemented in the data analysis part. Also, for data generalization, we design user input window, results display window and visualization window.