Chengqi Zhang

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This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms <i>A</i> &#8658; &#172; <i>B</i>, &#172; <i>A</i> &#8658; <i>B</i>, and &#172; <i>A</i> &#8658; &#172; <i>B</i>, which indicate negative associations between itemsets.(More)
We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model(More)
Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance. In this paper, we study the problem of(More)
Recognition of protein folding patterns is an important step in protein structure and function predictions. Traditional sequence similarity-based approach fails to yield convincing predictions when proteins have low sequence identities, while the taxonometric approach is a reliable alternative. From a pattern recognition perspective, protein fold(More)
The luminance of a natural scene is often of high dynamic range (HDR). In this paper, we propose a new scheme to handle HDR scenes by integrating locally adaptive scene detail capture and suppressing gradient reversals introduced by the local adaptation. The proposed scheme is novel for capturing an HDR scene by using a standard dynamic range (SDR) device(More)
Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&amp;D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the(More)
Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic(More)