| With the development of science and technology and the coming of a internet age, people's ability on collecting and storing data have been greatly improved by means of database techniques and DBMS. How to analyze these data efficiently, use them for forecasting and decision-making has become a question for discussion. When confronted with large scale, complicated databases and the requirement of updated analysis, traditional methods for forecasting and decision-making have quite a lot of disadvantages. However, the forecasting and decision-making methods based on data mining can complement such disadvantages effectively. And that is what this paper intend to discuss.The major works of the dissertation are as follows:(1) We reviewed the phylogeny, functions and flows of data mining and gave an outlook of the development of DM. Meanwhile, a summary about the similarities and differences of data mining and statistics has been given.(2) Analyze and research of data mining technique for association rules. Based on a systematic explain of its classical theory, this paper introduced the classical Apriori algorithms of association rules and some kinds of improved algorithms. Then, we talked about how to use R to apply these algorithms into practice.(3) Sequence pattern mining. Similarly, based on a systematic explain of its classical theory, we summarized the similarities and differences of association rules mining and sequence pattern mining. Just like the mining of association rules, corresponding algorithms are also needed when mining sequence patterns. So, we gave three kinds of algorithms.(4) Research of data mining technique for time series. Here we mainly discussed trend-analysis, time series similarity pattern mining based on the ARMA model and periodic analysis.(5) Finally, with some simulative data and by using R and Clementine, we discussed how to apply the theory of mining association rules into market basket analysis, in order to help decision-making for marketing. |
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