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Article citations


Ahmad, A. and Dey, L. (2007) A Method to Compute Distance between Two Categorical Values of Same Attribute in Unsupervised Learning for Categorical Data Set. Pattern Recognition Letters, 28, 110-118.

has been cited by the following article:

  • TITLE: Stock Pattern Mining and Correspondence Analysis Based on Association Rules

    AUTHORS: Xingru Yue, Feng Shi

    KEYWORDS: Stock, Meta Rule, Confidence, Lift, Correspondence Analysis

    JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.5 No.3, July 24, 2017

    ABSTRACT: In this paper, association rules were applied to mining patterns in stock K-line trend. The pattern which ordinary investors interested in is defined as T-RG (Three-Red Guards). In the mining process, we take the K-line in A-share markets as objects. Through the analysis, investors can select the appropriate point of purchase and selling point. With the help of T-RG, investors can better improve the chance of short-term investment success in A-share markets. In order to explore and validate the T-RG, the main contents of this paper include the following aspects: putting forward a method that judge the validity of rules based on confidence-lift; proposing the meta rule that corresponds to the pattern of T-RG; developing a computer program to extract the T-RG using MATLAB, which supports batch mining; leading fundamental factors into correspondence analysis with identification indexes; reminding the selected stocks, so as to verify the reliability of the identification indexes. According to the above research, something can be learned: In A-share markets, the higher the discriminant index value is, the less number of shares meeting the requirements is; the same discriminant index value, the stock proportion has difference among plates. Confidence P1, P2 and Lift are extremely related to the GC (General Capital), and Lift is extremely related to the Ind (Industry). In the GEM, confidence P1 of mid-cap is near [0.7,1], Lift is near (1,3), confidence P1 of the manufacturing industry is near [0.7,1].