داده کاوی مبتنی بر دانش اطلاعات اخبار در اینترنت با استفاده از نقشه های شناختی و شبکه های عصبی

In this paper, we investigate ways to apply news information on the Internet to the prediction of interest rates. We developed the Knowledge-Based News Miner (KBNMiner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the Internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates. This paper focuses on improving the performance of data mining by using prior knowledge. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant.

Nowadays, the capability to both generate and collect data has been expanded enormously and provides us with huge amounts of data. Millions of databases are being used in business data management, scientific and engineering data management, as well as other applications. Data mining has become a research area with increasing importance with the amount of data greatly increasing (Changchien and Lu, 2001, Chiang et al., 2000, Fayyads et al., 1996 and Park et al., 2001). Furthermore, data mining has come to play an important role since research has come to improve many methods used in data mining applications including statistical pattern recognition, association rules, recognizing sequential or temporal patterns, clustering or segmentation, data visualization, and classification. Although most data is stored in a database from which it can readily be applied to a data mining application, some kinds of data such as news information is not. As the popularity of the World Wide Web increases, many newspapers expand their services by providing news information on the web in order to be more competitive and increase benefits. The web disseminates real time news to investors. News information includes articles on the political situation, social conditions, international events, government policies, trader's psychology, and all those topics, which we see and understand through the Internet. Such information is formulated in the form of texts, referred to as documents, and thus text mining is required if the information is to be applied in data mining applications. Many researchers attempt to predict interest rates by using the time series model (Bidarkota, 1998), neural networks model (Hong & Han, 1996), the integrated model of neural networks and case-based reasoning (Kim & Noh, 1997). Meanwhile another approach was attempted in the prediction of the stock price index where Kohara, Ishikawa, Fukuhara, and Nakamura (1997) took into account non-numerical factors such as political and international events from newspaper information. They insist that, with event information acquired from newspapers, this method improves prediction ability of neural network. Although they personally read newspapers and rated each political and international event according to their judgment, it is, however, not easy for people to search and retrieve the vast amount of news simply through his/her knowledge and capacity. So we propose a means of applying news information from the Internet for the prediction of interest rates. The system discussed here, named the Knowledge-Based News Miner (KBNMiner), is designed to adopt a prior knowledge base, representing expert knowledge, as a foundation on which to probe and collect news and then to apply this news information to a neural network model for interest rate predictions. A cognitive map (CM) is used to build the prior knowledge base. CM is a representation perceived to exist by a human being in a visible or conceptual target world. CM manages the causality and relation of non-numeric factors mentioned earlier. The KBNMiner retrieves the event information from news information on the web utilizing CM and prior knowledge. Event information is divided into two types in the KBNMiner. One is positive event information, which affects the increase of interest rates, and the other is negative event information, which affects the decrease of interest rates. A neural network model is developed and experimented on using event information. This study focuses on the effect news information can have on the prediction of interest rates. As discussed earlier, the event information, which is acquired by the KBNMiner, is applied into a neural network model for the validation of our suggested method. More, specifically, the following research question is addressed: • What is the effect of the event information on the neural network performance when compared to other prediction models with no event information such as the neural network and random walk models? In Section 2, we provide a brief overview of data mining and discuss the CM method employed in KBNMiner and the way to build prior knowledge with CMs. Section 3 introduces the architecture of KBNMiner and presents a detailed description of KBNMiner. In Section 4, interest rate prediction data is used to illustrate the performance of KBNMiner. And we present the results of our approach and analyze the results statistically. Finally, the conclusion is presented.

The KBNMiner was developed as a means of applying current information acquired on the World Wide Web in the prediction of interest rates. The KBNMiner provides traders or those who are concerned about the movement of interest rates with more relevant knowledge from data and aids in effective decision-making. It is designed not only to apply expert specialist knowledge, but also knowledge of events and conditions influencing interest rate dynamics. The process involves the formation of a prior knowledge base, derived from the CMs of professional experience and learning, upon which the system draws in the search and retrieval news information to further be applied in a neural network model capable of predicting interest rates. The empirical results of our experiments show improvements in performance, when the information news is applied to the neural network. The pared t-test was performed and we attained significant improvement of the neural network performance statistically. The research question ‘what is the effect of applying event information on the performance of data mining applications’ is answered here, in one form, as we have explained how data mining systems with prior knowledge are statistically more effective. We have also described how to apply prior knowledge to data mining systems. Furthermore, our study shows that the CM is a useful tool for representing knowledge and reflecting the causality of knowledge. Our methods need to refine CMs and to improve the algorithm of the IR system for acquiring more correct results. The more progressive approach should be considered in future although our methods are designed and developed from conservative points, which accompany minimal errors and risks