Study And Application Of Clustering Analysis In Data Mining

Data Mining is a superior area in the information and database technology, and is commonly considered as one of the key technology with wild developing perspective. Data Mining relates to statistics, AI (especially machine learning), fuzzy theory, database technology etc, huge quantity data and retractile of algorithm are emphasized, it is also a technology approaching applied, so it is difficult to realize.Clustering analysis is one of the main functions of Data Mining. As the development of Data Mining, a number of clustering algorithms have been founded, for example Partitioning methods, Hierarchical methods, Density-based methods, Grid-based methods, Model-methods etc. These methods have related to all of fields of AN science, and have got great effect in specific fields and states, but these all meet difficulties when processing huge quantity data with complex data type.In this thesis, we discussed the concept, function type, processing procedure and technology algorithms of Data Mining. The main technology of Data Mining is Data Mining algorithm, so we analyzed and compared several Data Mining algorithms in this thesis, and did some research work on the K-means algorithm, DBSCAN algorithm, and proposed the algorithm for mining outlier data based on the distance. Based on student's grade data of a college in Shanxi province, we also used above algorithm to have clustering analysis, and realized information visualization. Finally, we discovered some valuable knowledge.