Cluster Analysis In Applied Research, Scientific Data Mining

It's a real challenge for us to make Data Mining algorithm easier to use in our project, The information in raw data is in short of organization, and full of a mass of noise, and on the other side, people want to obtain the information quickly and accurately. Clustering analysis is an important part of the whole Data Mining system. Clustering is the process of grouping the data into classes or clusters so that objects within the same cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Clustering analysis is the method which partition class to the clustered objects as required of thing's characteristics. Clustering processes are always carried out in the condition with no pre-known knowledge, so the most research task is to solve that how to get the clustering result in this premises.This thesis aims to discuss the clustering techniques with the background of large-scale nuclear physics Science Data Mining. First, we introduce the key techniques and the main task in Data Mining, then we analyze the data preprocessing techniques and clustering techniques combine Data Mining techniques with Science Data. From data preprocessing aspect, we propose some methods of Segmenting, Denoising, Integrating and Transforming, and we use"truncation method"and"successive difference method"in data reduction, at last we extract information from the science data. In the field of clustering, By comparing some clustering methods and analysing characteristic of Science Data, we propose an improved hierarchical clustering method synthetic idea of k-means method. This method has some specialities: bring the data cluster center; using similarity as clustering distance,...