Research Of Data Mining Method: Association And Trend Analysis

This dissertation mainly studies two methods of Data Mining and consists of five components: introduction, the summarization of Data Mining, the research of associate analysis, the research of trend analysis and conclusion.In the introduction, the reason of studying Data Mining is given: conflict between the exploring and updating of data with lacking of effective information and knowledge that people need in wise decision. Then the development and evolvement of Data Mining are given. In the end of the introduction the prospect of Data Mining is discussed.In chapter 1, the definition of Data Mining, the category of methods of Data Mining, the front of Data Mining and the deference and correlation of Data Mining and statistics are discussed detailed. First, investigating two necessary definitions in the definition of Data Mining: having the true data and facing the real problem. The result of two classify of methods of Data Mining that are discussed in this paper lead to the important position in the research of Data Mining. Then summarizing the status of research in Data Mining from eight aspects. Last, in discussing the relation of Data Mining and statistics, the sameness and distinguish between them are given.Chapter 2 Mainly discussing the classic method of associate analysis and the method of negative associate analysis that based on interest measurement. Through a case the false rule that concluded from the classic method of associate analysis that based on "support-confidence". While analyses the same case using the method of negative associate analysis that based on interest, we can see that this method can mine more applicable and more interesting data for the user. In order to apply the method of negative associate analysis this method has the as its interest measurement and modify the classic method: Apriori method.Chapter 3 Based on encoding the transaction data item set, the original data istransformed into a series of integral variable and this series is proved to be a Markov chain in theory. And instead of transfer probability, frequency is used in transfer probability matrix. Then analysis for the sale data of supermarket indicates that the method is fine, and a nice result that customs choice is same to different bland of one product is received.In the conclusion part, the author summaries the research of Data Mining 's method.