Research And Improvement Of An Algorithm For Time-Series Patterns Discovery

Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data, which is deemed to one of a foreland of data mining system and a promising cross-subject. Association rule is one of more important part in data mining, and finding frequent item sets plays an important role in abstracting association rules. Most of existing algorithms cost much time and space because they need plenty of repetition.Data mining algorithm will directly influence the finding knowledge. At present, most of researches focus on the algorithm and application of data mining. According to requirement and actuality of data mining field, mining technology of time-series database in complex database is studied in this paper. I study on mostly finding time-series patterns in time-series data.The field and subject basic theory knowledge of time-series patterns finding and the actuality of research are summarized, and the meaning of its research is analyzed. The detailed works are as follows: the finding patterns problems in the time-series data sequence are described, and a new trend logic expression method is introduced, and its algorithm and experiment result of algorithm are given; time-scries data are disposed, and using the arctg. slope of line as the sample of pattern recognition, so ignoring the aberrance of pattern in the classified. In addition, a new time-series pattern finding algorithm based on higher-order neural network is put forward. In this algorithm, author used two technology - the higher-order neural network of single_ layer and hierarchy. Higher-order neural network of single_layer has no trouble of hiding layer node, so training speed is fast; Ability of pattern classified increases; The classification accuracy of pattern is improved without local optimality problem; Hierarchy can make training speed of each node neural network, and improve the classified accuracy of pattern. The two technologies make the classified effect of the algorithm better. The thinking of the algorithm is provided and performed.The result of experiment indicates that the algorithm which author put toward improve the classified accuracy.The algorithm that the paper provides may be applied in the field of time-series data. Comparing with the other time-series pattern finding algorithm, this one has a few of innovations. Firstly, improving time-series pattern finding algorithm from the classified accuracy. Secondly, ignoring some aberrance of time-series pattern by the high-order neural network. At last, improving the classified accuracy by the classified way.