Duong Tuan Anh

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The course timetabling problem of large-scale size in realistic applications is considered very hard and cannot be solved by exact methods. In this paper, we present a solution method for this timetabling problem using local search methods. The solution method consists of two phases: the first phase to provide an initial solution that satisfies all hard(More)
Efficient and accurate similarity searching on a large time series data set is an important but non- trivial problem. In this work, we propose a new approach to improve the quality of similarity search on time series data by combining symbolic aggregate approximation (SAX) and piecewise linear approximation. The approach consists of three steps:(More)
Among several algorithms have been proposed to solve the problem of time series discord discovery, HOT SAX is one of the widely used algorithms. In this work, we employ state-of-the-art iSAX representation in time series discord discovery. We propose a new time series discord discovery algorithm, called HOTiSAX, by employing iSAX rather than SAX(More)
In this paper, we introduce a new time series dimensionality reduction method, IPIP. This method takes full advantages of PIP (Perceptually Important Points) method, proposed by Chung et al., with some improvements in order that the new method can theoretically satisfy the lower bounding condition for time series dimensionality reduction methods.(More)
Among several existing algorithms proposed to solve the problem of time series discord discovery, HOT SAX and WAT are two widely used algorithms. Especially, WAT can make use of the multi-resolution property in Haar wavelet transform. In this work, we employ state-of-the-art iSAX representation rather than SAX representation in WAT algorithm. To apply iSAX(More)
Time series forecasting is very important in several domains and has received a lot of interest from researchers in recent years. In this paper, we investigate the use of pattern matching technique in seasonal or trend time series prediction. First, this technique retrieves the sequence prior to the interval to be forecasted. Then this sequence is used as a(More)
This paper presents a novel approach for time series clustering which is based on BIRCH algorithm. Our BIRCH-based approach performs clustering of time series data with a multi-resolution transform used as feature extraction technique. Our approach hinges on the use of cluster feature (CF) tree that helps to resolve the dilemma associated with the choices(More)