Study On Associationrules Algorithms And Its Application In Stock Market

Data Mining aims to get previously unknown and potentially useful knowledge from a large amount of data. Association rule mining is the most developing, main and vigorous research content in Data Mining. With the development of the stock market, lots of history exchange data have been stored in database. It attracts more and more attention that how to use these history exchange data to discover the rules of the stock market. Especially at the latter half of 2005 year, stock market was happened to reform. Chinese stock market renerves and surges high tide one wave after another. The stock data of this period time become well excavating object for Data Mining. A mass of valuable rules will be discoveried to direct investors.Exploration of algorithms plays an important role in all Data Mining research. Data Mining faces large database. The efficiency of algorithms is the most important, so it is very significant to research and improve the existing algorithms. Based on above, this thesis mainly studies the algorithms of association rule mining. Firstly, it generally introduces Data Mining, including the concepts and the patterns, main mining problems, system classifications, and the application and development trend. Secondly, this thesis researches the Association Rule Algorithm totally, which is important in Data Mining. It analyses the classical algorithms that are Apriori, AprioriTid, AprioriHybird algorithms and the improved algorithms of Apriori, and it summarizes existing problems in these algorithms. Then this thesis presents an improved AprioriHybird algorithm in detail, which is one of the key contents, and compares it with the AprioriHybird algorithm.In order to discovery the stock market information well, we must combine stock market characteristic, especially operational rules of stock itself. The movement of stock includes thinking and wisdom of tens of thousands of people. We want to study it only through detailed and patient observation. By a long time studying andtracking stock market and simulated operation, this thesis determines to describe stock from macroscopical and microcosmic aspects. On the macroscopical aspect, data of latest months is transformed to obtain the long-term parameters of the stock through the fuzzy time series match method. On the microcosmic aspect, data of latest days is transformed to obtain the short-term parameters of the stock through the correlative books and the simulated combat. They form a set of integrated parameters sets, and build the solid foundation for the mining work. This is the second key content of this thesis.Finally the disposal of stock data, the improvement of algorithm and mining were completed under VC++6.0 platform. The experiments show that the efficiency of the improved AprioriHybird was superior to AprioriHybird algorithm to a certain extent. And a lot of association rules were extracted, some of them have fine instructional significance.