Kuang Yu Huang

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This study proposes a method of cluster validity index that simultaneously provide the measurements of goodness of clustering on clustered data and of classification accuracy for complicated information systems based upon the PBMF-index method and rough set (RS) theory. The maximum value of this index, called the Huang-index, not only provides the best(More)
This paper introduces a new hybrid cluster validity method based on particle swarm optimization, for successfully solving one of the most popular clustering/classifying complex datasets problems. The proposed method for the solution of the clustering/classifying problem, designated as PSORS index method, combines a particle swarm optimization (PSO)(More)
This study proposes a method, designated as the GRP-index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and(More)
In the past, the choices of b values to be applied to find the b-reducts in VPRS for an information system are somewhat arbitrary. In this study, a systematic method which bridges the fuzzy set methodology and probabilistic approach of RS to solve the threshold value b determination problem in variable precision rough sets (VPRS) is proposed. Different from(More)
A Rough Set (RS) based dataset reduction method using SWARM optimization algorithm and a cluster validation function is proposed. In the proposed approach, the user specifies the classification quality required in advance, and the method then finds the attribute reducts and perform attribute discretization to satisfy the desired quality of classification.(More)
In this study, the Grey Relational Analysis (GRA) model is combined with Fuzzy C-Means (FCM) clustering scheme and Rough Set (RS) theory to create an automatic portfolio selection mechanism. In the proposed approach, 53 financial indices are collected automatically for each stock item every quarter and a GRA model is used to consolidate these indices into(More)
In this study, the new grey relational grade (GRG) method is combined with moving average autoregressive exogenous (ARX) prediction model, GM(1,N) theory and rough set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed approach, financial data are collected automatically every quarter and are input(More)