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In the era of digital information, the size of data collection has been growing significantly. Knowledge results in term of association rules obtained from the set of data are numerous and hard to select. This paper proposes the approach for selecting the interesting subsets of association rules from big association results. The selective criterion is based on well-known interesting measures including...
The stock index indicators, e.g., Moving Average (MA), Relative Strength Index (RSI), Price Rate of Change (PROC), Moving Average Convergence Divergence (MACD), or Stochastic Oscillator (STH) are often used as the main factor in trading. Stock traders decide to trade by using these indicators. This paper applies theory of rough set to find the hidden relationship among these indicators which affects...
Association rule mining can be used to discover interesting rules from large databases easily for better decision making in most real world applications except the financial market. This is because the investors are interested in high profit and low risk trading results more than in those of high confidence and high support. Based on a working model of profit mining, we propose an effective algorithm...
Stock trading is a popular approach for money investment. This paper focuses on Thai stock market which has hundreds of individual stocks. The aim of this paper is to apply an association rule mining technique for discovering the relationships between individual stocks. The transactional dataset used to generate the rules consists of 242 trading days from 4 January 2010 to 30 December 2010. The results...
Reviews on Web can help small investors make decision in selecting funds. The size of fund reviews is smaller than other products, which proposes a challenge to extract sentiment by using statistic methods. We develop a methodology to deal with this problem by using association rule to select seed words and introducing new outside resources to improve the traditional PMI performance. The result shows...
With the rapid development of networks and information technology, the endless information has paid more and more attention by people. While in the pursuit of information with high speed, the analysis and mining of the information and rules hidden deep in the data are also paid more emphasis. Data mining technology is to organize and analyze the data, which can extract and discover knowledge from...
Knowledge discovery in financial databases has important implications. Decision making process on financial datasets is known to be difficult because of the complex knowledge domain and specific statistical characteristics of the data. In this paper, we investigate the decision making problem on financial datasets such as stock market fluctuations by means of financial ratio measurements while maintaining...
Mining association rules from transactions occurred at different time series is a difficult task because of high computational complexity, very large database size and multidimensional attributes. Traditional techniques, such as fundamental and technical analysis can provide investors with tools for predicting stock prices. However, these techniques cannot discover all the possible relations between...
The association rule has become one of the most important techniques in data mining. New algorithms must be developed in order to apply it to more areas. This paper proposes association rule algorithms for logical equality relationships, modified from the original Apriori and FP-Growth algorithms. Logical equality is defined as truerarrtrue (1rarr1) or falserarrfalse (0rarr0) associations. This special...
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