Huge Amounts Of Data Based On The Density Of Incremental Mining

Incremental data mining is updating the result of data mining incrementally, when data increase in the large data set (such as database or datahouse), it is not updating the total data set. For many kind of large databases or datahouse, incremental data mining is a temptable goal. We study the incremental data mining technology based outlier factor.We first describe the basic concepts and basic method and introduce the commonly objects and representative applications; and we study clustering data mining technology and describe the commonly rules, and we introduce the incremental data mining method; so we accumulate experience for farther study and definitude requirement.The influence of the algorithm parameters is very notability and the parameters need the appoint of users in mass clustering data mining algorithm, so determining parameters is very difficulty. We bring forward clustering algorithm based outlier factor, and resolve the problem efficiency, and we gained the incremental algorithm on the base. We describe the concepts of clustering algorithm based outlier factor, and explain the idea of the algorithm and the clustering process.In the end, we analysis the validity of the algorithm, and we contrast the algorithm with the other; we analysis and validate that the parameters have littler influence to clustering data mining algorithm based outlier factor; and we also analysis and validate the incremental clustering data mining algorithm.