Hung Long Nguyen

Work place: Faculty of Economic Information System, Vietnam Commercial University (VCU)



Research Interests: Computer systems and computational processes, Systems Architecture, Data Mining, Data Structures and Algorithms


Hung Long Nguyen is currently a lecturer at Faculty of Economic Information System, Vietnam Com-mercial University (VCU). He recei-ved his B.Sc. degree in Informatics from Hanoi University of Science in 1991, and his M.Sc. degree in Infor-mation Technology from Le Quy Don Technical University in 2002. His research interests include Data Mining, Knowledge Discovery in Databases, Information Systems, and Database.

Author Articles
An Efficient Algorithm in Mining Frequent Itemsets with Weights over Data Stream Using Tree Data Structure

By Hung Long Nguyen Thuy Nguyen Thi Thu Giap Cu Nguyen

DOI:, Pub. Date: 8 Nov. 2015

In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve the data stream problems. The disadvantage of these algorithms is that they have to seek all data stream many times and generate a large set of candidates. In this paper, we have proposed a process of mining frequent itemsets with weights over a data stream. Based on the downward closure property and FP-Growth method [8,9] an alternative algorithm called WSWFP-stream has been proposed. This algorithm is proved working more efficiently regarding to computing time and memory aspects.

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An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights

By Hung Long Nguyen

DOI:, Pub. Date: 8 Oct. 2015

Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams.

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