| dc.description.abstract | In the perspective of investments, they desire to use the less financial variables to anticipate the most performance information. Every human being is increasingly faced with unmanageable amounts of financial data; hence, data mining or knowledge discovery apparently affects all of us. In this study of mining performance of company, we attempt to summarize the stronger characteristic rules of fundamental analysis using 81 financial statement variables. To address this problem, we proposed an effective method, a Char Algorithm, to automatically produce characteristic rules to describe the major characteristics of data in a table is proposed. To fit the data type of Char Algorithm, we proceed many steps to preprocess source data of financial statement from 2001-2003. In the first step, data compression, we adapt wavelet methods to preprocess time series data of several attributes from financial statement from 2001-2003. After data to be compressed by wavelet technique, the second step, sliding window, processes in order to increase the amount of virtual data. Thirdly, we use cluster method to do data discretization process categorizing data to fit the discrete data type.
It is a difficult task to construct a concept tree to describe the financial statement. In contrast to traditional Attribute Oriented Induction methods, the algorithm, named as Char Algorithm, does not need a concept tree and only requires setting a desired coverage threshold to generate a minimal set of characteristic rules to describe the given dataset. We develop a formal framework for financial data to adapt Char Algorithm and afford advisements to investors to extract characteristic rules, rapidly. It is also our observation that the dimension of growth rate is significant in circumstance of generalizing good performance companies. | en_US |