A Structural Method Of Data Mining Based On Quotient Space And Its Applications

With the development of computer's pattern recognition technology, its applications have been extended ceaselessly. It is able to recognize these problems, such as financial engineer forecasting and multimedia database searching based on contents. Not only these problems have a feature space of high dimensionality and a data set with large amount of samples that belong to many different classes, but also the system involves many factors and is imperfection information systems. In addition, there is noise information that is difficulty to recognize. In the dissertation, the following methods are propose to solve these problems as far as possible:1.A useful scheme to solve these problems is proposed. Complex problems are represented by different granules based quotient space. After learning rules of different granules achieved, integrate rules of the complex problem can be gained by composing relative rules.2.How to gain learning rules of the same granule, a multi-side increasing by degrees algorithm (MIDA) is proposed. The best advantage of the covering algorithm is to reflect a sample set distribution truly. Three problems that are necessary to be analyzed further are proposed. First is to solve the conflict between validity and extend ability. Second is to recognize noise information by improving covering algorithm. The last is how to decrease the number of the covering domain. In addition the thought based on a covering algorithm may be used in selecting feature and analyze principal component. MIDA improves on the old cover algorithm and reduces conflict between validity and extending ability some way.3.Based on the structural machine learning, a probability decisional data-mining algorithm (DDMR) is proposed by composing relative rules of multi data sources that are built in quotient on topological. For objects with a high dimensionality feature space and a data set with large amount of samplesthat belong to many different classes, it is useful to divide and discompose them according to MIDA. For a complex database or data warehouse, it may be thought a complex object that its multi sides are defined. So we can use the same method to analyze, deal with, and recognize both based on the quotient space.It is stated in theory that stock market is generated from a very complex nonlinear dynamical system. As a result, it is necessary to replace the traditional statistic model with nonlinear model that can deal with imperfective information in order to improve the quality of forecasting stock market. In this dissertation, the structural machine learning algorithm is used in the quotient space of analyzing stock market. The main work includes:1.Combining the real problem of forecasting stock, the quotient space of analyzing stock market is constructed in this paper. And author applies MIDA and DDMR to forecasting time sequence.2.For problems to forecast sequence, it is important and effective to use data directly and not change them artificially in order to mine true rules of the object. For collecting stock data, we recognize and classify them in according to a defining period of time or exchanging volume, the result of our experiment is satisfying. Therefore the method proposed in the dissertation is applied widely.